Publications

2017

  • Ninghang Hu, Gwenn Englebienne, Zhongyu Lou, and Ben Kröse. Learning to recognize human activities using soft labels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
  • Ahmed Nait Aicha, Gwenn Englebienne, and Ben Kröse. Unsupervised visit detection in smart homes. Pervasive and Mobile Computing, 34:157–167, 2017.
  • Saskia Robben, Gwenn Englebienne, and Ben Kröse. Delta features from ambient sensor data are good predictors of change in functional health. IEEE Journal of Biomedical and Health Informatics, 2017.

2016

  • Sumit Mehra, Tessa Dadema, Ben J. A. Kröse, Bart Visser, Raoul H. H. Engelbert, Jantine Van Den Helder, and Peter J. M. Weijs. Attitudes of older adults in a group-based exercise program toward a blended intervention; a focus-group study. Frontiers in Psychology, 7:1827, 2016.
  • Margriet Pol, Fenna van Nes, Margo van Hartingsveldt, Bianca Buurman, Sophia de Rooij, and Ben Kröse. Older peoples perspectives regarding the use of sensor monitoring in their home. The Gerontologist, 56(3):485{493, 2016.
  • SMB Robben, MC Pol, BM Buurman, and BJA Kröse. Expert knowledge for modeling functional health from sensor data. Methods of Information in Medicine, 55(6):516–524, 2016.
  • Nazli Cila, Guido Jansen, Maarten Groen, Wouter Meys, Lea den Broeder, and Ben Kröse. Look! a healthy neighborhood: Means to motivate participants in using an app for monitoring community health. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pages 889-898. ACM, 2016.
  • Aduen Darriba Frederiks, Ben JA Kröse, and Gijs Huisman. Internet of touch: analysis and synthesis of touch across wearable and mobile devices. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pages 273-276. ACM, 2016.
  • Joey van der Bie, Britte Visser, Jordy Matsari, Mijnisha Singh, Timon van Hasselt, Jan Koopman, and Ben Kröse. Guiding the visually impaired through the environment with beacons. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pages 385-388. ACM, 2016.
  • Ninghang Hu, Aaron Bestick, Gwenn Englebienne, Ruzena Bajscy, and Ben Kröse. Human intent forecasting using intrinsic kinematic constraints. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016  pages 787-793. IEEE, 2016.
  • Saskia Robben, Ahmed Nait Aicha, and Ben Kröse. Measuring regularity in daily behavior for the purpose of detecting Alzheimer. In 10th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2016.
  • Nicky Nibbeling, Joey van der Bie, Ben Kröse, and Marije Baart De La Faille-Deutekom. Motiveren tot bewegen met beweeg-apps als BAMBEA. In Dag van Sportonderzoek 2016, pages 113-115. Hanze Hogeschool Groningen, 2016.
  • Joan Dallinga, Joey van der Bie, Ben Kröse, and Marije Baart de la Faille Deutekom. Intelirun: Ontwikkeling van een evidence-based en gepersonaliseerde hardloop-app. In Dag van Sportonderzoek 2016, page 35, 2016.
  • Joan Dallinga, Mark Janssen, Joey van der Bie, Nicky Nibbeling, Ben Kröse, Jos Goudsmit, Carl Megens, Marije Baart de la Faille Deutekom, and Steven Vos. De rol van innnovatieve technologie in het stimuleren van sport en bewegen in de steden amsterdam en eindhoven. Vrijetijdsstudies, volume 34, pages 43-57. NRIT Media, 2016.

2015

  • Ninghang Hu, G. Englebienne, Zhongyu Lou, and B. Kröse. Latent hierarchical model for activity recognition. IEEE Transactions on Robotics, 31(6):1472-1482, Dec 2015.
  • Tim van Oosterhout, Gwenn Englebienne, and Ben Kröse. RARE: people detection in crowded passages by range image reconstruction. Machine Vision and Applications, 26(5):561-573, 2015.
  • Albert Ali Salah, Ben JA Kröse, and Diane J Cook. Human Behavior Understanding: 6th International Workshop, HBU 2015, Osaka, Japan, Proceedings, volume 9277 of Lecture Notes on Computer Science. 2015.
  • Ahmed Nait Aicha, Gwenn Englebienne, and Ben Kröse. Continuous gait velocity analysis using ambient sensors in a smart home. In Ambient Intelligence, pages 219–235. Springer, 2015.
  • Joey van der Bie and Ben Kröse. Happy running? In Ambient Intelligence, pages 357-360. Springer, 2015.
  • Ninghang Hu, Gwenn Englebienne, Zhongyu Lou, and Ben Kröse. A hierarchical representation for human activity recognition with noisy labels. In 2015 IEEE/RSJ
    International Conference on Intelligent Robots and Systems (IROS),  pages 2517-2522. IEEE, 2015.
  • Albert Ali Salah, Ben JA Kröse and Diane J Cook. Behavior analysis for elderly. In Human Behavior Understanding, pages 1-10. Springer, 2015.
  • Ben Kröse. Analysis of home health sensor data. In Joost van Hoof, George Demiris, and Eveline J.M. Wouters, editors, Handbook of Smart Homes, Health Care and Well-Being, pages 1-10. Springer International Publishing, 2015.
  • Marije Kanis, Saskia Robben, and Ben Kröse. How are you doing? enabling older adults to enrich sensor data with subjective input. In Human Behavior Understanding, pages 39-51. Springer, 2015.
  • Ben Kröse. Sensoren en it: de zorg op de schop. de Automatiseringsgids, pages 22-23, february 2015.

2014

  • Ahmed Nait Aicha, Gwenn Englebienne & Ben Kröse. Modeling Visit Behaviour in Smart Homes using Unsupervised Learning. UBICOMP ’14 Adjunct proceedings, ACM, Seattle, pp 1193-1200, (2014)
  • Saskia Robben, Margriet Pol, Ben Kröse. Longitudinal Ambient Sensor Monitoring for Functional Health Assessments: A Case Study, UBICOMP 14 Adjunct proceedings ACM , Seattle, 1209-1216 (2014)
  • Marije Kanis & Ben Kröse. Slimme systemen voor de toekomst, Hogeschool van Amsterdam, Amsterdam (2014)
  • Francisco Ordonez, Gwenn Englebienne, Paula de Toledo, Tim van Kasteren, Araceli Sanchis, and Ben Kröse. Bayesian inference in hidden Markov models for in-home activity recognition. IEEE Pervasive Computing, 13(3), pp 67-75, July-Sept 2014.
  • Kristin Rieping, Gwenn Englebienne, and Ben Kröse. Behavior analysis of elderly using topic models. Pervasive and Mobile Computing 15:181-199, 2014.
  • Margriet Pol, Fenna van Nes, Margo van Hartingsveldt, Bianca Buurman, Sophia de Rooij, and Ben Krose. Older peoples perspectives regarding the use of sensor monitoring in their home. The Gerontologist, page gnu104, 2014.
  • Antoine Hogenboom, Iskander Smit, and Ben Kröse. A digital coach for self-tracking athletes. In Proceedings of Eurohaptics, 2014.
  • Ninghang Hu, Gwenn Englebienne, Zhongyu Lou, and Ben Kröse. Learning latent structure for activity recognition. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2014.
  • Ninghang Hu, Richard Bormann, Thomas Zwolfer, and Ben Kröse. Multi-User Identication and Ecient User Approaching by Fusing Robot and Ambient Sensors. In Proc. IEEE International Conference on Robotics and Automation (ICRA), 2014.
  • Ninghang Hu, Zhongyu Lou, Gwenn Englebienne, and Ben Kröse. Learning to recognize human activities from soft labeled data. Robotics: Science and Systems (RSS). IEEE, 2014.
  • Ninghang Hu, Gwenn Englebienne, and Ben Kröse. A two-layered approach to recognize high-level human activities. In Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (ROMAN). IEEE, 2014.
  • Ben J.A. Kröse, Tim van Oosterhout, and Gwenn Englebienne. Video surveillance for behaviour monitoring in home health care. In Measuring Behavior, 2014.

2013

  • Margriet C. Pol, Soemitro Poerbodipoero, Saskia Robben, Joost Daams, Margo van Hartingsveldt, Rien de Vos, Sophia E. de Rooij, Ben Kröse, and Bianca M. Buurman. Sensor monitoring to measure and support daily functioning for independently living older people: A systematic review and roadmap for further development. Journal of the American Geriatrics Society. 61(12), pp 2219-2227, December 2013.
  • Farshid Amirabdollahian, Sandra Bedaf, Richard Bormann, Heather Draper, Vanessa Evers, Jorge Gallego Perez, Gert Jan Gelderblom, Carolina Gutierrez Ruiz, David Hewson, Ninghang Hu, Ben Kröse, et al. Assistive technology design and development for acceptable robotics companions for ageing years. Paladyn, Journal of Behavioral Robotics, 4(2):94{112, December 2013.
  • Vijay John, Gwenn Englebienne, and Ben J. A. Kröse. Person reidentication using height-based gait in colour depth camera. In International Conference of Image Processing, pages 3345-3349, 2013.
  • Ninghang Hu, Gwenn Englebienne, and Ben Kröse. Posture recognition with a top-view camera. In International Conference on Intelligent Robots and Systems (IROS), pages 2152-2157. IEEE, 2013.
  • Ben Kröse (2013) Hoe gaat het met mij? Gezondheidsgegevens via smartphones, sensoren en social media. De informatiemaatschappij van 2023, G.J. van Bussel (Ed) , HvA, lectoraat Digital Archiving & Compliance , 128-135
  • Marise Schot, Miriam Reitenbach, Ron Boonstra, Saskia Robben, Pascal Wiggers, Margriet Pol, Marije Kanis, et al. (2013), Innovation in health care: Together with end users. Health-lab , Amsterdam, NL
  • Ahmed Nait Aicha, Gwenn Englebienne & Ben Kröse (2013) How lonely is your grandma? Detecting the visits to assisted living elderly from wireless sensor network data. Adjunct proceedings of UbiComp ’13 , ACM , 1285-1294
  • Vijay John, Gwenn Englebienne, and Ben J. A. Kröse. Solving person re-identication in non-overlapping camera using ecient Gibbs sampling. In British Machine Vision Conference, 2013.
  • Saskia Robben and Ben Krose (2013) Longitudinal Residential Ambient Monitoring: Correlating Sensor Data to Functional Health Status. Pervasive Health 2013, Venice, Italy.
  • Marije Kanis, Saskia Robben, Judith Hagen, Anne Bimmerman, Natasja Wagelaar & Ben Kröse (2013) Sensor monitoring in the home: Giving voice to elderly people. Proceedings of Pervasive Health ’13 , Venice, Italy
  • Saskia Robben, Mario Boot, Marije Kanis & Ben Kröse (2013) Identifying and visualizing relevant deviations in longitudinal sensor patterns for care professionals. Pervasive Health’13 International workshop on lifelogging for pervasive health , Venice, Italy
  • Josemans, W., Englebienne, G. & Kröse, B.J.A. (2013). Fusion of Color and Depth Camera Data for Robust Fall Detection. In S. Battiato & J. Braz (Eds.), Proceedings of the 8th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2013) (pp. 608-613). SCITEPRESS – Science and Technology Publications.
  • G. Huisman, A. Darriba Frederiks, E.M.A.G. Van Dijk, B.J.A. Kröse, and D.K.J. Heylen. The TaSST – Tactile Sleeve for Social Touch. In Proceedings of World Haptics Conference (WHC) 2013, pages 211-216, Deajon, Korea, 2013. IEEE.
  • Huisman, G., Darriba Frederiks, A., Dijk, E.M.A.G. van, Kröse, B.J.A. & Heylen, D.K.J. (2013). Self Touch to Touch Others: Designing the Tactile Sleeve for Social Touch. In S. Jordà & N. Parés (Eds.), online proceedings of TEI’13, Seventh International Conference on Tangible, Embedded and Embodied Interaction. Barcelona.

2012

2011

  • Aicha, N. & Kröse, B.J.A. (2011). Toepassing van Ambient Intelligent Systems in het HBO projectonderwijs. In Nederlands Informatica Onderwijs Congres NIOC (pp. 183-188).
  • Alizadeh, S., Bakkes, S.C.J., Kanis, M., Rijken, M. & Krose, B.J.A. (2011). Telemonitoring for Assisted Living Residences: The Medical Specialists’ View. In M. Jordanova & F. Lievens (Eds.), Proceedings of the Med-e-Tel 2011; The International eHealth, Telemedicine and Health ICT Forum for Educational, Networking and Business (pp. 75-78).
  • Bakkes, S.C.J., Morsch, R. & Kröse, B.J.A. (2011). Telemonitoring for Independently Living Elderly: Inventory of Needs & Requirements. In J. Maitland, J.C. Augusto & B. Caulfield (Eds.), Proceedings of the Pervasive Health 2011 conference (pp. 152-159).
  • Booij, O. (2011, november 25). View-based mapping for wheeled robots. UvA Universiteit van Amsterdam (149 pag.). Prom./coprom.: prof.dr.ir. F.C.A. Groen & prof.dr.ir. B.J.A. Krose.
  • Gacem, B., Vergouw, R., Verbiest, H., Cicek, E., Oosterhout, T. van, Krose, B. & Bakkes, S. (2011). Gesture recognition for an exergame prototype. In Proceedings of the BNAIC 2011, the 23rd Benelux Conference on Artificial Intelligence (pp. 457-458). Ghent, Belgium.
  • Hung, H.S. & Krose, B.J.A. (2011). Detecting F-formations as Dominant Sets. In Proceedings of International Conference on Multimodal Interaction (pp. 233-238). Alicante, Spain.
  • Marije Kanis and Sean Alizadeh and Jesse Groen and Milad Khalili and Saskia Robben and Sander Bakkes and Ben Kröse (2011). Ambient Monitoring from an Elderly-Centred Design Perspective: What, Who and How. Proceedings of the International Joint Conference on Ambient Intelligence (AMI-11), pp 324-329.
  • Kasteren, T.L.M. van, Englebienne,G. and & Kröse, B.J.A (2011).
    Human activity recognition from wireless sensor network data: Benchmark and software.
    In Jit Biswas Jesse Hoey Liming Chen, Chris Nugent (editors):
    Activity Recognition in Pervasive Intelligent Environments
    pages 165–186, 201
  • Kasteren, T.L.M. van (2011, april 27). Activity Recognition for Health Monitoring Elderly using Temporal Probabilistic Models. Ph.D. thesis, UvA Universiteit van Amsterdam. Prom./coprom.: prof.dr.ir. F.C.A. Groen & dr. ir. B.J.A. Kröse.
  • Kröse, B.J.A. & Mil, R. van (2011). ‘Slimme leefomgeving vereist meer ict-kennis’ Verwarming en ventilatie, 66(10), 524-527.
  • Kröse, B.J.A., Oosterhout, T.J.M. van & Kasteren, T.L.M. van (2011). Activity monitoring systems in health care. In A.A. Salah & T. Gevers (Eds.), Computer Analysis of Human Behavior (pp. 325-346). Springer-Verlag London Limited.
  • Leeuwen, H. van, Teeuw, W., Tangelder, R., Griffioen, R., Kröse, B.J.A. & Schouten, B. (2011). Ervaringen met ICT-onderzoek in het HBO. In Nederlands Informatica Onderwijs Congres NIOC (pp. 165-167).
  • Oosterhout, T.J.M. van, Bakkes, S.C.J. & Kröse, B.J.A. (2011). Head Detection in Stereo Data for People Counting and Segmentation. In L. Mestetskiy & J. Braz (Eds.), Proceedings of 6th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application (VISIGRAPP 2011) (pp. 620-625)
  • Kasteren, T.L.M. van, Englebienne, G. & Kröse, B.J.A. (2011). Human Activity Recognition from Wireless Sensor Network Data: Benchmark and Software. In L. Chen, C. Nugent, J. Biswas & J. Hoey (Eds.), Activity Recognition in Pervasive Intelligent Environments (Ambient and Pervasive Intelligence) (pp. 165-186). Atlantis Press.

2010

  • Bakkes, S.C.J. & Kröse, B.J.A. (2010). Pervasive healthcare technology for assisted living residences. Journal of Gerontechnology9(2), pp 191–192, 2010
  • Booij, O., Zivkovic, Z. & Kröse, B.J.A (2010).
    Efficient probabilistic planar robot motion estimation given pairs of images.
    Robotics: Science and Systems VI,Zaragoza, Spain, June 2010, pp 1-10
  • Englebienne, G. & and Kröse, B.J.A (2010).
    Fast Bayesian people detection.
    Proceedings of the 22nd benelux AI conference (BNAIC 2010), (Best Paper Award) 2010.
  • Evers, V. and Kröse, B.J.A (2010).
    A motivational health companion in the home as part of an intelligent health monitoring sensor network.
    AFFINE 3rd International workshop on Affective Interaction in Natural Environments. ACM Multimedia 2010 , Firenze, Italy., October 2010.
  • Evers, V. and Kröse, B.J.A (2010). Toward an ambient empathic health companion for self care in the intelligent home.
    Proceedings of European Conference on Cognitive Ergonomics,Delft, the Netherlands, August 2010.
  • Heerink, M, Kröse, B.J.A, Evers, V., & B.J. Wielinga (2010).
    Relating conversational expressiveness to social presence and acceptance of an assistive social robot.
    Virtual Reality, Volume 14 , Issue 1, Pages: 77-84 .
  • Heerink, M, Kröse, B.J.A, Evers, V., & B.J. Wielinga (2010).
    Assessing Acceptance of Assistive Social Agent Technology by Older Adults: the Almere Model.
    International Journal of Social Roboticshttp://dx.doi.org/10.1007/s12369-010-0068-5.
  • Kasteren, T.L.M. van, Englebienne,G. and & Kröse, B.J.A (2010).
    Transferring knowledge of activity recognition across sensor networks.
    Pervasive Computing: 8th International Conference, Pervasive 2010,Finland, May 17-20, 2010, pp 283-300
  • Kasteren, T.L.M. van, Englebienne,G. and & Kröse, B.J.A (2010).
    Activity recognition using semi-markov models on real world smart home datasets.
    J. Ambient Intell. Smart Environ.,2(3):311–325, 2010.
  • Kasteren, T.L.M. van, Englebienne,G. and & Kröse, B.J.A (2010).
    An activity monitoring system for elderly care using generative and discriminative models.
    Personal and Ubiquitous Computing, 14 (6), pp 489-498, 2010.
  • Athanasios Noulas, Gwenn Englebienne, Bas Terwijn, and Ben Kröse (2010).
    Speaker detection for conversational robots using synchrony between audio and video.
    Proceedings ICRA 2010 Workshop Interactive Communication for Autonomous Intelligent Robots}2010.
  • Rijnboutt, J and Evers, V. and Kröse, B.J.A (2010).
    Cliënten willen meer controle over de camera.
    ICTZorg, pages 30 — 32, oktober 2010.

2009

  • Booij, O., Zivkovic, Z. & Kröse, B.J.A (2009).
    Efficient data association for view based SLAM using connected dominating sets.
    Robotics and Autonomous Systems 57(12):1225–1234.
  • Englebienne, G., Oosterhout, T.J.M. van & Kröse, B.J.A (2009).
    \newblock Tracking in sparse multi-camera setups using stereo vision.
    Proceedings of the 3rd ACM/IEEE International Conference on
    Distributed Smart Cameras}
  • Esteban,I., Booij, O., Zivkovic,Z. & Kröse, B.J.A (2009).
    Mapping large environments with an omnivideo camera.
    Proceedings of the Conf. On Simulation, Modeling and Programming for Autonomous Robots pages 297–306.
  • Heerink, M, Kröse, B.J.A, Evers, V., & B.J. Wielinga (2009).
    The influence of social presence on acceptance of an assistive social robot and screen agent by elderly users.
    Advanced Robotics, 23(14):1909–1923.
  • Heerink, M, Kröse, B.J.A, B.J. Wielinga, & Evers, V.(2009).
    Measuring acceptance of an assistive social robot: a suggested toolkit.
    Proceedings of Ro-man, Toyama,pp 528-533.
  • Heerink, Marcel, Kröse, Ben, Wielinga, Bob and Evers, Vanessa.(2009).
    Measuring the influence of social abilities on acceptance of an interface robot and a screen agent by elderly users.
    BCS HCI ’09: Proceedings of the 2009 British Computer Society Conference on Human-Computer Interaction, Cambridge, United Kingdom, pp 430–439.
  • Kasteren, T.L.M. van and & Kröse, B.J.A (2009).
    A sensing and annotation system for recording datasets in multiple homes.
    CHI 2009 workshop ‘Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research’: Proceedings.
  • Zivkovic, Z., Cemgil, A.T. & Krose, B.J.A. (2009). Approximate Bayesian methods for kernel-based object tracking. Computer Vision and Image Understanding, 113(6):743–749, 2009.


2008

  • Booij, O., Kröse, B., Peltason, J., Spexard, T. & Hanheide, M. (2008). Moving from augmented to interactive mapping. In Interactive learning – RSS 2008 workshop: [proceedings:] June 28, 2008, Z�rich, Switzerland (pp. [21]-[23]). Kaiserslautern: Deutsches Forschungsinstitut f�r K�nstliche Intelligenz.
  •  Booij, O., Zivkovic, Z. & Kröse, B. (2008). Sampling in image space for vision based SLAM. In Robotics: science and systems: workshop Inside Data Association: 28 June 2008, ETH Z�rich, Switzerland: publications (pp. [1]-[8]). Bremen: Transregional Collaborative Research Center Spatial Cognition: Reasoning, Action, Interaction.
  •  Gibson, C.H.S., Kasteren, T.L.M. van & Kröse, B.J.A. (2008). Monitoring Homes with Wireless Sensor Networks. In Proceedings of The International Educational and Networking Forum for eHealth, Telemedicine and Health ICT (Medetel08) (pp. 370-374).
  •  Hagethorn, F.N., Kröse, B.J.A., Greef, P. de & Helmer, M.E. (2008). Creating design guidelines for a navigational aid for mild demented pedestrians. In E. Aarts, J.L. Crowley, B. de Ruyter, H. Gerh�user, A. Pflaum, J. Schmidt & R. Wichert (Eds.), Ambient Intelligence: European Conference, AmI 2008, Nuremberg, Germany, November 19-22, 2008: Proceedings Lecture Notes in Computer Science (pp. 276-289). Berlin: Springer.
  •  Heerink, M., Kröse, B.J.A., Wielinga, B.J. & Evers, V. (2008). Enjoyment, Intention to Use and Actual Use of a Conversational Robot by Elderly People. In T. Fong & K. Dautenhahn (Eds.), Proceedings of the third ACM/IEEE International Conference on Human-Robot Interaction . (pp. 113-119) Amsterdam: ACM.
  • Heerink, M., Kröse, B., Wielinga, B. & Evers, V. (2008). Measuring perceived adaptiveness in a robotic eldercare companion. In HRI 2008: Robotic Helpers: User Interaction, Interfaces and Companions in Assistive and Therapy Robotics: Proceedings.
  • Heerink, M., Kröse, B., Evers, V. & Wielinga, B. (2008). The influence of perceived adaptiveness of a social agent on acceptance by elderly users. In Proceedings of ISG’08: The 6th International Conference of the International Society for Gerontechnology (pp. 57-61).
  • Heerink, M., Kröse, B., Evers, V. & Wielinga, B.J. (2008). The influence of social presence on acceptance of a companion robot by older people. Journal of Physical Agents, 2(2), 33-40.
  • Kasteren, T. van, Noulas, A., Englebienne, G. & Kröse, B. (2008). Accurate activity recognition in a home setting. In Proceedings of the 10th International Conference on Ubiquitous Computing: September 21-24, 2008, Seoul, Korea ACM International Conference Proceeding Series (pp. 1-9). New York, NY: Association for Computing Machinery (ACM).
  • Kröse, B.J.A., Kasteren, T.L.M. van, Gibson, C.H.S. & Dool, E.J. van den (2008). Care: context awareness in residences for elderly. In ISG 2008 – The 6th International Conference of the International Society for Gerontechnology (pp. 101-105). Pisa, Italy.
  • Kröse, B.J.A., Bierhoff, I. & Schilders, M. (2008). The Digital Life Centre: a Living Lab for Education in Real World Situations. In Proceedings of The International Educational and Networking Forum for eHealth, Telemedicine and Health ICT (Medetel08) (pp. 143-146).
  • Noulas, A.K., Kasteren, T. van & Kröse, B.J.A. (2008). A hybrid generative-discriminative approach to speaker diarization. In A. Popescu-Belis & R. Stiefelhagen (Eds.), Machine learning for multimodal interaction: 5th international workshop, MLMI 2008, Utrecht, The Netherlands, September 8-10, 2008: Proceedings Vol. 5237. Lecture Notes in Computer Science (pp. 98-109). Berlin: Springer.
  • Noulas, A.K. & Kröse, B.J.A. (2008). Deep Belief Networks for dimensionality reduction. In A. Nijholt, M. Pantic, M. Poel & H. Hondorp (Eds.), Proceedings of the twentieth Belgian-Dutch Conference on Artificial Intelligence BNAIC (pp. 185-191). Enschede: University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science.
  • Noulas, A.K. & Kröse, B.J.A. (2008). Deep architectures for Human Computer Interaction. In Proceedings of the Workshop on Affective Interaction in Natural Environments (AFFINE) (pp. 1-5).
  • Speelman, M. & Kröse, B. (2008). Virtual Mirror gaming in libraries. In A. Nijholt & R. Poppe (Eds.), Facial and bodily expressions for control and adaptation of games (ECAG 2008) (pp. 37-47). Enschede: Centre for Telematics and Information Technology (CTIT).
  • Veldkamp, D., Hagenthorn, F., Kröse, B.J.A. & Greef, P. de (2008). The Use of Visual landmarks in a Wayfinding System for Elderly with Beginning Dementia. In Proceedings of The International Educational and Networking Forum for eHealth, Telemedicine and Health ICT (Medetel08) (pp. 161-166).
  • Zivkovic, Z., Booij, O., Kröse, B.J.A. & Topp, E.A. (2008). From sensors to human spatial concepts: an annotated dataset. IEEE Transactions on Robotics and Automation, 24(2), 501-505.

2007

  • O. Booij, B. Terwijn, Z. Zivkovic and Ben J. A. Kröse (2007). Navigation Using an Appearance Based Topological Map IEEE International Conference on Robotics and Automation, pages 411-418, 2007
  • Heerink, M., Kröse, B.J.A., Wielinga, B.J. & Evers, V. (2007). Observing conversational expressiveness of elderly users interacting with a robot and screen agent. In Proceedings of the International Conference on Rehabilitation Robotics . pages 154-157, Amsterdam: ACM.
  • Heerink, M., Kröse, B.J.A., Wielinga, B.J. & Evers, V. (2007). iCat in Eldercare. In C Bartneck & T Kanda (Eds.), Proceedings of the 2nd ACM/IEEE International Conference on Human-Robot Interaction (pp. 177-184). Washington DC.
  • Kasteren, T.L.M. van & and Ben J. A. Kröse (2007). Bayesian activity recognition in residence for elderly IE’07: Proceedings of the third international Intelligent Environments conference.
  • Kasteren, T.L.M. van & and Ben J. A. Kröse (2007). Context awareness in residences for elders IEEE Pervasive Computing, 6(1) 59-60.
  • Kasteren, T.L.M. van, Kröse, B.J.A. & Cemgil, A.T. (2007). Realtime Simultaneous Tempo Tracking and Rhythm Quantization in Music. In Demo in BNAIC 2007: The 19th Belgian-Dutch Conference on Artificial Intelligence (pp. 431-432).
  • Kröse, B.J.A., Booij, O. & Zivkovic, Z. (2007). A geometrically constrained image similarity measure for visual mapping, localization and navigation. In Proceedings of the 3rd European Conference on Mobile Robots (pp. 168-174). Freiburg, Germany.
  • Mensink, T., Kröse, B.J.A. & Zajdel, W.P. (2007). Distributed Appearance Based Tracking using the EM algorithm. In Proceedings of the 2007 First ACM/IEEE International Conference on Distributed Smart Cameras (pp. 178-184). Vienna, Austria: IEEE.
  • Noulas, A. & Kröse, B.J.A. (2007). Learning in Multi-Modal Information Streams. In Proceedings of the 19th Belgian-Dutch Conference on Artificial Intelligence 2007 (pp. 245-252). Utrecht, The Netherlands.
  • Noulas, A. & Kröse, B.J.A. (2007). On-line Multi-Modal Speaker Diarization. In Proceedings of International Conference on Multimodal Interfaces ’07 (pp. 350-358). Nagoya, Japan.
  • Noulas, A., Vlassis, N. & Kröse, B.J.A. (2007). Cross Entropy for learning in Multi-Modal Streams. In Proceeding of the Joint Workshop on MultiModal Interaction and Related Machine Learning Algorithms ’07 . Brno, Czech Republic.
  • Terwijn, B. & Noulas, A. (2007). BNAIC Demo: Online Speaker Detection by the iCat Robot. In BNAIC 2007: The 19th Belgian-Dutch Conference on Artificial Intelligence (pp. 451-452).
  • Z. Zivkovic and Ben J. A. Kröse (2007). Part based people detection using 2D range data and images in: IEEE/RSJ International Conference on Intelligent Robots and Systems
  • Zivkovic, Z. & Kröse, B.J.A. (2007). Part Based People Detection on a Mobile Robot. In Proceedings of IEEE ICRA2007 Workshop: From features to actions .
  • Z. Zivkovic, O. Booij , and Ben J. A. Kröse (2007). From images to rooms Robotic and Autonomous Systems, vol.55, no.5, pages 411-418, 2007

2006

  • Heerink, M., Kröse, B.J.A., Wielinga, B.J. & Evers, V. (2006). Studying the acceptance of a robotic agent by elderly users. International Journal of Assistive Robotics and Mechatronics, 7(3), 25-35.
  • Wojciech Zajdel, A. Taylan Cemgil and Ben J. A. Kröse (2006). Dynamic Bayesian Networks for Visual Surveillance with Distributed Cameras in: Smart Sensing and Context 240-243.
  • Heerink, M., Kröse, B.J.A., Wielinga, B.J., & Evers, V. (2006). Studying the acceptance of a robotic agent by elderly users International Journal of Assistive Robotics and Mechatronics, 7(3), 25-35.
  • Booij, O., Zivkovic, Z., & Kröse, B.J.A. (2006). From sensors to rooms. In Proc. IROS Workshop From Sensors to Human Spatial Concepts (pp. 53-58). IEEE.
  • Booij, O., Zivkovic, Z., & Kröse, B.J.A. (2006). Sparse appearance based modeling for robot localization. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (pp. 1510-1515). ieee.

    2008

    • Booij, O., Kröse, B., Peltason, J., Spexard, T. & Hanheide, M. (2008). Moving from augmented to interactive mapping. In Interactive learning – RSS 2008 workshop: [proceedings:] June 28, 2008, Z�rich, Switzerland (pp. [21]-[23]). Kaiserslautern: Deutsches Forschungsinstitut f�r K�nstliche Intelligenz.
    •  Booij, O., Zivkovic, Z. & Kröse, B. (2008). Sampling in image space for vision based SLAM. In Robotics: science and systems: workshop Inside Data Association: 28 June 2008, ETH Z�rich, Switzerland: publications (pp. [1]-[8]). Bremen: Transregional Collaborative Research Center Spatial Cognition: Reasoning, Action, Interaction.
    •  Gibson, C.H.S., Kasteren, T.L.M. van & Kröse, B.J.A. (2008). Monitoring Homes with Wireless Sensor Networks. In Proceedings of The International Educational and Networking Forum for eHealth, Telemedicine and Health ICT (Medetel08) (pp. 370-374).
    •  Hagethorn, F.N., Kröse, B.J.A., Greef, P. de & Helmer, M.E. (2008). Creating design guidelines for a navigational aid for mild demented pedestrians. In E. Aarts, J.L. Crowley, B. de Ruyter, H. Gerh�user, A. Pflaum, J. Schmidt & R. Wichert (Eds.), Ambient Intelligence: European Conference, AmI 2008, Nuremberg, Germany, November 19-22, 2008: Proceedings Lecture Notes in Computer Science (pp. 276-289). Berlin: Springer.
    •  Heerink, M., Kröse, B.J.A., Wielinga, B.J. & Evers, V. (2008). Enjoyment, Intention to Use and Actual Use of a Conversational Robot by Elderly People. In T. Fong & K. Dautenhahn (Eds.), Proceedings of the third ACM/IEEE International Conference on Human-Robot Interaction . (pp. 113-119) Amsterdam: ACM.
    • Heerink, M., Kröse, B., Wielinga, B. & Evers, V. (2008). Measuring perceived adaptiveness in a robotic eldercare companion. In HRI 2008: Robotic Helpers: User Interaction, Interfaces and Companions in Assistive and Therapy Robotics: Proceedings.
    • Heerink, M., Kröse, B., Evers, V. & Wielinga, B. (2008). The influence of perceived adaptiveness of a social agent on acceptance by elderly users. In Proceedings of ISG’08: The 6th International Conference of the International Society for Gerontechnology (pp. 57-61).
    • Heerink, M., Kröse, B., Evers, V. & Wielinga, B.J. (2008). The influence of social presence on acceptance of a companion robot by older people. Journal of Physical Agents, 2(2), 33-40.
    • Kasteren, T. van, Noulas, A., Englebienne, G. & Kröse, B. (2008). Accurate activity recognition in a home setting. In Proceedings of the 10th International Conference on Ubiquitous Computing: September 21-24, 2008, Seoul, Korea ACM International Conference Proceeding Series (pp. 1-9). New York, NY: Association for Computing Machinery (ACM).
    • Kröse, B.J.A., Kasteren, T.L.M. van, Gibson, C.H.S. & Dool, E.J. van den (2008). Care: context awareness in residences for elderly. In ISG 2008 – The 6th International Conference of the International Society for Gerontechnology (pp. 101-105). Pisa, Italy.
    • Kröse, B.J.A., Bierhoff, I. & Schilders, M. (2008). The Digital Life Centre: a Living Lab for Education in Real World Situations. In Proceedings of The International Educational and Networking Forum for eHealth, Telemedicine and Health ICT (Medetel08) (pp. 143-146).
    • Noulas, A.K., Kasteren, T. van & Kröse, B.J.A. (2008). A hybrid generative-discriminative approach to speaker diarization. In A. Popescu-Belis & R. Stiefelhagen (Eds.), Machine learning for multimodal interaction: 5th international workshop, MLMI 2008, Utrecht, The Netherlands, September 8-10, 2008: Proceedings Vol. 5237. Lecture Notes in Computer Science (pp. 98-109). Berlin: Springer.
    • Noulas, A.K. & Kröse, B.J.A. (2008). Deep Belief Networks for dimensionality reduction. In A. Nijholt, M. Pantic, M. Poel & H. Hondorp (Eds.), Proceedings of the twentieth Belgian-Dutch Conference on Artificial Intelligence BNAIC (pp. 185-191). Enschede: University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science.
    • Noulas, A.K. & Kröse, B.J.A. (2008). Deep architectures for Human Computer Interaction. In Proceedings of the Workshop on Affective Interaction in Natural Environments (AFFINE) (pp. 1-5).
    • Speelman, M. & Kröse, B. (2008). Virtual Mirror gaming in libraries. In A. Nijholt & R. Poppe (Eds.), Facial and bodily expressions for control and adaptation of games (ECAG 2008) (pp. 37-47). Enschede: Centre for Telematics and Information Technology (CTIT).
    • Veldkamp, D., Hagenthorn, F., Kröse, B.J.A. & Greef, P. de (2008). The Use of Visual landmarks in a Wayfinding System for Elderly with Beginning Dementia. In Proceedings of The International Educational and Networking Forum for eHealth, Telemedicine and Health ICT (Medetel08) (pp. 161-166).
    • Zivkovic, Z., Booij, O., Kröse, B.J.A. & Topp, E.A. (2008). From sensors to human spatial concepts: an annotated dataset. IEEE Transactions on Robotics and Automation, 24(2), 501-505.

    2007

    • O. Booij, B. Terwijn, Z. Zivkovic and Ben J. A. Kröse (2007). Navigation Using an Appearance Based Topological Map IEEE International Conference on Robotics and Automation, pages 411-418, 2007
    • Heerink, M., Kröse, B.J.A., Wielinga, B.J. & Evers, V. (2007). Observing conversational expressiveness of elderly users interacting with a robot and screen agent. In Proceedings of the International Conference on Rehabilitation Robotics . pages 154-157, Amsterdam: ACM.
    • Heerink, M., Kröse, B.J.A., Wielinga, B.J. & Evers, V. (2007). iCat in Eldercare. In C Bartneck & T Kanda (Eds.), Proceedings of the 2nd ACM/IEEE International Conference on Human-Robot Interaction (pp. 177-184). Washington DC.
    • Kasteren, T.L.M. van & and Ben J. A. Kröse (2007). Bayesian activity recognition in residence for elderly IE’07: Proceedings of the third international Intelligent Environments conference.
    • Kasteren, T.L.M. van & and Ben J. A. Kröse (2007). Context awareness in residences for elders IEEE Pervasive Computing, 6(1) 59-60.
    • Kasteren, T.L.M. van, Kröse, B.J.A. & Cemgil, A.T. (2007). Realtime Simultaneous Tempo Tracking and Rhythm Quantization in Music. In Demo in BNAIC 2007: The 19th Belgian-Dutch Conference on Artificial Intelligence (pp. 431-432).
    • Kröse, B.J.A., Booij, O. & Zivkovic, Z. (2007). A geometrically constrained image similarity measure for visual mapping, localization and navigation. In Proceedings of the 3rd European Conference on Mobile Robots (pp. 168-174). Freiburg, Germany.
    • Mensink, T., Kröse, B.J.A. & Zajdel, W.P. (2007). Distributed Appearance Based Tracking using the EM algorithm. In Proceedings of the 2007 First ACM/IEEE International Conference on Distributed Smart Cameras (pp. 178-184). Vienna, Austria: IEEE.
    • Noulas, A. & Kröse, B.J.A. (2007). Learning in Multi-Modal Information Streams. In Proceedings of the 19th Belgian-Dutch Conference on Artificial Intelligence 2007 (pp. 245-252). Utrecht, The Netherlands.
    • Noulas, A. & Kröse, B.J.A. (2007). On-line Multi-Modal Speaker Diarization. In Proceedings of International Conference on Multimodal Interfaces ’07 (pp. 350-358). Nagoya, Japan.
    • Noulas, A., Vlassis, N. & Kröse, B.J.A. (2007). Cross Entropy for learning in Multi-Modal Streams. In Proceeding of the Joint Workshop on MultiModal Interaction and Related Machine Learning Algorithms ’07 . Brno, Czech Republic.
    • Terwijn, B. & Noulas, A. (2007). BNAIC Demo: Online Speaker Detection by the iCat Robot. In BNAIC 2007: The 19th Belgian-Dutch Conference on Artificial Intelligence (pp. 451-452).
    • Z. Zivkovic and Ben J. A. Kröse (2007). Part based people detection using 2D range data and images in: IEEE/RSJ International Conference on Intelligent Robots and Systems
    • Zivkovic, Z. & Kröse, B.J.A. (2007). Part Based People Detection on a Mobile Robot. In Proceedings of IEEE ICRA2007 Workshop: From features to actions .
    • Z. Zivkovic, O. Booij , and Ben J. A. Kröse (2007). From images to rooms Robotic and Autonomous Systems, vol.55, no.5, pages 411-418, 2007

    2006


    2005

    • B.Bakker, Z.Zivkovic,
      and B.J.A. Kröse.Hierarchical dynamic programming for robot path planning.
      In Proc. IEEE/RSJ International Conference on Intelligent Robots and
      Systems
      , pages 3720-3725, 2005.
      (PDF, 243 Kbytes)
    • O.Booij, Z.Zivkovic,
      and B.Kröse.Pruning the image set for appearance based robot localization.
      In Proceedings of the Annual Conference of the Advanced School for
      Computing and Imaging
      , pages 57-64, June 2005.
      (PDF, 276 Kbytes)
    • A.T. Cemgil,
      W.Zajdel, and B.Kröse.
      A hybrid graphical model for robust feature extraction from video.
      In C.Schmid, S.Soatto, and C.Tomasi, editors, IEEE Computer Vision and
      Pattern Recognition (CVPR)
      , pages 1158-1165, San Diego, June 2005.
      (PDF, 300 Kbytes)
    • G.Klaassen,
      W.Zajdel, and B.J.A. Kröse.
      Speech-based localization of multiple persons for an interface robot.
      In Proc. of IEEE Int. Conference on Computational Intelligence in
      Robotics and Automation (CIRA2005)
      , pages 47-52, 2005.
      (PDF, 657 Kbytes)
    • B.J.A. Kröse.Digital life: de toegevoegde waarde van ICT in onze
      leefomgeving
      .
      HvA publicaties. Amsterdam University Press, 2005.
      in Dutch.
      (PDF, 1960 Kbytes)
    • B.J.A. Kröse.
      Digital life is extra hulp in zorgsector.
      de Automatiseringsgids, 34:13, 2005.
      in Dutch.
      (PDF, 27 Kbytes)
    • J.M. Porta, J.J. Verbeek, and B.J.A. Kröse.Active appearance-based robot localization using stereo vision.Autonomous Robots, 18(1):59-80, 2005.(PDF, 2262 Kbytes)
    • J.M. Porta and
      B.J.A Kröse.
      Appearance-based concurrent map building and localization.
      Robotics and Autonomous Systems, 54(2):159-164, 2005.
      ISBN 0921-8890.
      (PDF, 1120 Kbytes)
    • J.J. Verbeek, N.Vlassis, and B.J.A. Kröse.Self-organizing mixture models.Neurocomputing, 63:99-123, 2005.(PDF, 859 Kbytes)
    • W.Zajdel and
      B.J.A. Kröse.
      A sequential bayesian algorithm for surveillance with non-overlapping
      cameras
      .
      Int. Journal of Pattern Recognition and Artificial Intelligence,
      19(8):977-996, 2005.
      (PDF, 568 Kbytes)
    • W.Zajdel, N.Vlassis,
      and B.J.A Kröse.
      Bayesian methods for tracking and localization.
      In E.Aarts, J.Korts, and W.Verhaegh, editors, Intelligent
      Algorithms
      , pages 243-258. Kluwer Academic Publishers, 2005.
      (PDF, 166 Kbytes)
    • W.Zajdel, Z.Zivkovic,
      and B.J.A. Kröse.
      Keeping track of humans: have I seen this person before?.
      In Proc. of Int. Conference on Robotics and Automation (ICRA),
      pages 2093-2098, 2005.
      (Gzipped PostScript, 6 pages, 1179 Kbytes)
      (PDF, 1517 Kbytes)
    • Z.Zivkovic
      and B.J.A. Kröse.
      On matching interest regions using local descriptors – can an information
      theoretic approach help?
      .
      In Proc. British Machine Vision Conference, pages 50-58, 2005.
      (PDF, 241 Kbytes)
    • Z.Zivkovic,
      B.Bakker, and B.J.A. Kröse.Hierarchical map building using visual landmarks and geometric
      constraints
      .
      In Proc. IEEE/RSJ International Conference on Intelligent Robots and
      Systems
      , pages 7-12, 2005.
      (PDF, 550 Kbytes)

    2004

    • B.Kröse,
      R.Bunschoten, S.ten Hagen, B.Terwijn, and N.Vlassis.
      Household robots look and learn.
      IEEE Robotics and Automation Magazine, 11(4):45-52, December
      2004.
    • B.J.A. Kröse,
      N.Vlassis, and W.Zajdel.
      Bayesian methods for tracking and localization.
      In Proc. of Philips Symposium On Intelligent Algorithms, (SOIA),
      pages 27-38, 2004.
      (Gzipped PostScript, 12 pages, 184 Kbytes)
      (PDF, 167 Kbytes)
    • J.M. Porta and
      B.J.A. Kröse.
      Appearance-based concurrent map building and localization.
      In F.C.A. Groen, editor, International Conference on Intelligent
      Autonomous Systems, IAS’04
      , pages 1022-1029. IOS Press, March 2004.
      ISBN 1-58603-414-6.
    • J.M. Porta and
      B.J.A. Kröse.
      Appearance-based concurrent map building and localization using a
      multi-hypotheses tracker.
      .
      In Proc.IEEE/RSJ International Conference on Intelligent Robots and
      Systems
      , pages 3424-3429, Sendai, Japan, 2004. IEEE Press.
      (PDF, 156 Kbytes)
    • Martijn Reuvers,
      Richard Kleihorst, Harry Broers, and Ben Kröse.
      A smart camera for face recognition.
      In Proceedings of SPS-2004, 2004.
      (PDF, 225 Kbytes)
    • S.H.G. ten
      Hagen and B.J.A. Kröse.
      Learning to understand tasks for mobile robots.
      In Proc. of the IEEE Int. Conf. on System, Man and Cybernetics,
      The Hague, The Netherlands, October 2004.
      To Appear.
      (Gzipped PostScript, 6 pages, 467 Kbytes)
      (PDF, 448 Kbytes)
    • J.M. Terwijn,
      B.Porta and B.J.A. Kröse.
      A particle filter to estimate non-markovian states.
      In F.C.A. Groen, editor, International Conference on Intelligent
      Autonomous Systems, IAS’04
      , pages 1062-1069. IOS Press, March 2004.
      ISBN 1-58603-414-6.
    • W.Zajdel, A.T. Cemgil,
      and B.Kröse.
      Online multicamera tracking with a switching state-space model.
      In Proc. of IEEE International Conference on Pattern Recognition
      (ICPR)
      , pages IV:339-343, Cambridge, UK, 2004.
      (PDF, 175 Kbytes)
    • Z.Zivkovic
      and B.Kröse.
      An EM-like algorithm for color-histogram-based object tracking.
      In IEEE Conference on Computer Vision and Pattern Recognition,
      June 2004.
      To appear.
      (PDF, 372 Kbytes)
    • Z.Zivkovic
      and B.Kröse.
      A probabilistic model for an EM-like object tracking algorithm using
      color-histograms
      .
      In 6th IEEE International Workshop on Performance Evaluation of Tracking
      and Surveillance (in connection with ECCV2004)
      , May 2004.
      To appear.
      (PDF, 171 Kbytes)

    2003

    • R.Bunschoten and B.Kröse.
      Robust scene reconstruction from an omnidirectional vision system.
      IEEE Transactions on Robotics and Automation, 19(2):351-357,
      2003.
      (PDF, 886 Kbytes)
    • Roland
      Bunschoten and Ben Kröse.
      Visual odometry from an omnidirectional vision system.
      In Proceedings of the International Conference on Robotics and Automation
      ICRA’03
      , pages 577-583, Taipei, Taiwan, 2003.
      ISBN 0-7803-7737-0.
    • B.J.A. Kröse,
      J.M. Porta, K.Crucq, A.J.N. van Breemen, M.Nuttin, and E.Demeester.
      Lino, the user-interface robot.
      In E.Aarts, R.Collier, E.van Loenen, and B.D. Ruyter, editors,
      Proceedings of the First European Symposium on Ambience Intelligence
      (EUSAI)
      , pages 264-274, Eindhoven, The Netherlands, November 2003.
      Springer.
      ISBN 3-540-20418-0.
      (PDF, 7004 Kbytes)
    • J.M. Porta and
      B.J.A. Kröse.
      Vision-based localization for mobile platforms.
      In E.Aarts, R.Collier, E.van Loenen, and B.D. Ruyter, editors,
      Proceedings of the First European Symposium on Ambience Intelligence
      (EUSAI)
      , pages 208-219, Eindhoven, The Netherlands, November 2003.
      Springer.
      ISBN 3-540-20418-0.
      (PDF, 2051 Kbytes)
    • JosepM. Porta
      and Ben Kröse.
      On the use of disparity maps for robust robot localization under different
      illumination conditions
      .
      In A.T. deAlmeida and U.Nunes, editors, Proceedings of the 11th
      International Conference on Advanced Robotics, ICAR’03
      , pages
      124-129, Coimbra, Portugal, June 30-July 3 2003. IEEE Press.
      ISBN 972-96889-9-0.
      (Gzipped PostScript, 6 pages, 345 Kbytes)
      (PDF, 988 Kbytes)
    • J.M. Porta, J.J.
      Verbeek, and B.J.A. Kröse.
      Enhancing appearance-based robot localization using sparse disparity
      maps
      .
      In C.S.G. Lee and J.Yuh, editors, Proc.IEEE/RSJ International
      Conference on Intelligent Robots and Systems
      , pages 980-985, Las
      Vegas, USA, October 2003. IEEE Press.
      ISBN 0-7803-7861-X.
      (PDF, 253 Kbytes)
    • JosepM. Porta, Bas
      Terwijn, and Ben Kröse.
      Efficient entropy-based action selection for appearance-based robot
      localization
      .
      In Proceedings of the International Conference on Robotics and Automation
      ICRA’03
      , pages 2842-2847, Taipei, Taiwan, 2003.
      ISBN 0-7803-7737-0.
      (PDF, 114 Kbytes)
    • Stephan ten
      Hagen and Ben Kröse.
      Neural Q-learning.
      Neural Computing & Applications, 12(2):81-88, November 2003.
      ISSN: 0941-0643 (Paper) 1433-3058 (Online).
      (Gzipped PostScript, 13 pages, 163 Kbytes)
      (PDF, 248 Kbytes)
    • S.H.G. ten
      Hagen and B.J.A. Kröse.
      Learning to navigate using a lazy map.
      In A.T. deAlmeida and U.Nunes, editors, Proceedings of the 11th
      International Conference on Advanced Robotics, ICAR’03
      , pages
      299-304, Coimbra, Portugal, June 30-July 3 2003.
      (Gzipped PostScript, 6 pages, 157 Kbytes)
      (PDF, 119 Kbytes)
    • A.J.N van
      Breemen, K.Crucq, B.J.A Kröse, M.Nuttin, J.M. Porta, and E.Demeester.A user-interface robot for ambient intelligent environments.
      In P.Fiorini, editor, Proceedings of the 1st International Workshop on
      Advances in Service Robotics, ASER’03
      , pages 132-139, Bardolino,
      Italy, 2003. Fraunhofer IRB Verlag.
      (Gzipped PostScript, 8 pages, 5288 Kbytes)
      (PDF, 3287 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.J.A. Kröse.
      Efficient greedy learning of Gaussian mixture models.
      Neural Computation, 15(2):469-485, 2003.
      (PDF, 505 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.J.A. Kröse.
      Non-linear feature extraction by the coordination of mixture models.
      In S.Vassiliadis, L.M.J. Florack, J.W.J. Heijnsdijk, and A.vander Steen,
      editors, Proc. 8th Ann. Conf. of the Advanced School for Computing and
      Imaging (ASCI)
      , pages 287-293, Heijen, The Netherlands, June 2003.
      ASCI.
      (PDF, 1331 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.J.A. Kröse.
      Self-organization by optimizing free-energy.
      In M.Verleysen, editor, Proc. of European Symposium on Artificial Neural
      Networks
      , pages 125-130. D-side, Evere, Belgium, 2003.
      (PDF, 184 Kbytes)
    • A.H.G. Versluis,
      B.J.F Driessen, J.A. van Woerden, and B.J.A. Kröse.
      Enhancing the usability of the MANUS manipulator by using visual
      servoing
      .
      In Proceedings of International Conference on Rehabilitation Robotics,
      ICORR 2003
      , pages 43-46, KAIST, Daejon, South Korea, 22-25 April
      2003.
    • Wojciech
      Zajdel and Ben Kröse.
      Approximate learning and inference for tracking with non-overlapping
      cameras
      .
      In M.H. Hamza, editor, Proc. of the IASTED Int. Conf. on Artificial
      Intelligence and Applications
      , pages 70-75. ACTA Press, Calgary,
      Canada, 2003.
      (PDF, 125 Kbytes)
    • Wojciech
      Zajdel and Ben Kröse.
      Gaussian mixture model for multi-sensor tracking.
      In T.Heskes, P.Lucas, L.Vuurpijl, and W.Wiegerinck, editors,
      Proceedings of the 15th Dutch-Belgian Artificial Intelligence
      Conference, BNAIC’03
      , pages 371-378, Nijmegen, The Netherlands,
      October 2003. Elsevier.
      (PDF, 200 Kbytes)

    2002

    • R.Bunschoten and B.Kröse.
      3-D scene reconstruction from cylindrical panoramic images.
      Robotics and Autonomous Systems (special issue), 41(2/3):111-118,
      November 2002.
      (PDF, 225 Kbytes)
    • B.J.A. Kröse,
      N.Vlassis, and R.Bunschoten.
      Omnidirectional vision for appearance-based robot localization.
      In G.D. Hagar, H.I. Cristensen, H.Bunke, and R.Klein, editors, Sensor
      Based Intelligent Robots: International Workshop, Dagstuhl Castle, Germany,
      October 2000, Selected Revised Papers
      , number 2238 in Lecture Notes in
      Computer Science, pages 39-50. Springer, 2002.
      (PDF, 730 Kbytes)
    • S.H.G. ten
      Hagen and B.J.A. Kröse.
      Towards global consistent pose estimation from images.
      In R.Siegwart and C.Laugier, editors, Proc.IEEE/RSJ International
      Conference on Intelligent Robots and Systems
      , pages 466-471,
      Lausanne,Switzerland, September 2002. Omnipress.
      (Gzipped PostScript, 6 pages, 317 Kbytes)
      (PDF, 176 Kbytes)
    • S.H.G. ten
      Hagen and B.J.A. Kröse.
      Trajectory reconstruction for self-localization and map building.
      In W.R. Hamel and A.A. Maciejewski, editors, Proc. IEEE Int. Conf. on
      Robotics and Automation
      , pages 1796-1801, Washington D.C., USA, May
      2002. Omnipress.
      (Gzipped PostScript, 6 pages, 168 Kbytes)
      (PDF, 143 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.Kröse.
      A k-segments algorithm for finding principal curves.
      Pattern Recognition Letters, 23(8):1009-1017, 2002.
      (Gzipped PostScript, 12 pages, 97 Kbytes)
      (PDF, 149 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.J.A. Kröse.
      Coordinating Principal Component Analyzers.
      In J.R. Dorronsoro, editor, Proceedings of International Conference on
      Artificial Neural Networks
      , Lecture Notes in Computer Science,
      pages 914-919, Madrid, Spain, August 2002. Springer.
      (PDF, 251 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.J.A. Kröse.
      Fast non-linear dimensionality reduction using topology preserving
      networks
      .
      In M.Verleysen, editor, Proc. of European Symposium on Artificial Neural
      Networks
      , pages 193-198. D-side, Evere, Belgium, 2002.
      (PDF, 167 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.J.A. Kröse.
      Locally linear generative topographic mapping.
      In M.Wiering, editor, Benelearn 2002: Proceedings of the Twelfth
      Belgian-Dutch Conference on Machine Learning
      , Utrecht, The
      Netherlands, December 2002.
      (PDF, 158 Kbytes)
    • N.Vlassis,
      Y.Motomura, and B.Kröse.
      Supervised dimension reduction of intrinsically low-dimensional data.
      Neural Computation, 14(1):191-215, January 2002.
      (Gzipped PostScript, 22 pages, 331 Kbytes)
    • N.Vlassis,
      B.Terwijn, and B.Kröse.
      Auxiliary particle filter robot localization from high-dimensional sensor
      observations
      .
      In W.R. Hamel and A.A. Maciejewski, editors, Proc. IEEE Int. Conf. on
      Robotics and Automation
      , pages 7-12, Washington D.C., USA, May 2002.
      Omnipress.
      (Gzipped PostScript, 6 pages, 218 Kbytes)
      (PDF, 178 Kbytes)
    • W.Zajdel and
      B.Kröse.
      Bayesian network for multiple hypothesis tracking.
      In H.Blockeel and M.Denecker, editors, Proceedings of the 14th
      Dutch-Belgian Artificial Intelligence Conference, BNAIC’02
      , pages
      379-386, Leuven, Belgium, October 2002.
      (Gzipped PostScript, 8 pages, 52 Kbytes)
      (PDF, 74 Kbytes)
    • Spexard, T., Li, S., Wrede, B., Fritsch, J., Sagerer, G., Booij, O., Zivkovic, Z., Terwijn, B., & Kröse, B.J.A. (2006). BIRON, where are you? – Enabling a robot to learn new places in a real home environment by integrating spoken dialog and visual localization. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (pp. 934-940). ieee.
    • M.Heerink, B.J.A.
      Kröse, B.J. Wielinga, and V.Evers.
      Human-robot user studies in eldercare: Lessons learned.
      In Proc. Int. Conf. on Smart Homes and Health Telematics, Belfast,
      Northern Ireland, June 2006(pp. 31-38)
    • Heerink, M., Kröse, B.J.A., Wielinga, B.J., & Evers, V. (2006). The Influence of a Robot’s Social Abilities on Acceptance by Elderly Users In In Proceedings RO-MAN (pp. 521-526). Hertfordshire.
    • K.L. Koay, Z.Zivkovic,
      B.Kröse, K.Dautenhahn, M.L. Walters, N.R. Otero, and
      A.Alissandrakis.
      Methodological issues of annotating vision sensor data using subjects’ own
      judgement of comfort in a robot human following experiment
      .
      In IEEE International Symposium on Robot and Human Interactive
      Communication, to appear
      , 2006.
    • Z.Zivkovic,
      B.Bakker, and B.Kröse.
      Hierarchical map building and planning based on graph partitioning.
      In IEEE International Conference on Robotics and Automation, pages
      803-809, 2006.
      (PDF, 391 Kbytes)

    2005

    • B.Bakker, Z.Zivkovic,
      and B.J.A. Kröse.Hierarchical dynamic programming for robot path planning.
      In Proc. IEEE/RSJ International Conference on Intelligent Robots and
      Systems
      , pages 3720-3725, 2005.
      (PDF, 243 Kbytes)
    • O.Booij, Z.Zivkovic,
      and B.Kröse.Pruning the image set for appearance based robot localization.
      In Proceedings of the Annual Conference of the Advanced School for
      Computing and Imaging
      , pages 57-64, June 2005.
      (PDF, 276 Kbytes)
    • A.T. Cemgil,
      W.Zajdel, and B.Kröse.
      A hybrid graphical model for robust feature extraction from video.
      In C.Schmid, S.Soatto, and C.Tomasi, editors, IEEE Computer Vision and
      Pattern Recognition (CVPR)
      , pages 1158-1165, San Diego, June 2005.
      (PDF, 300 Kbytes)
    • <G.Klaassen,
      W.Zajdel, and B.J.A. Kröse.
      Speech-based localization of multiple persons for an interface robot.
      In Proc. of IEEE Int. Conference on Computational Intelligence in
      Robotics and Automation (CIRA2005)
      , pages 47-52, 2005.
      (PDF, 657 Kbytes)
    • B.J.A. Kröse.Digital life: de toegevoegde waarde van ICT in onze
      leefomgeving
      .
      HvA publicaties. Amsterdam University Press, 2005.
      in Dutch.
      (PDF, 1960 Kbytes)
    • B.J.A. Kröse.
      Digital life is extra hulp in zorgsector.
      de Automatiseringsgids, 34:13, 2005.
      in Dutch.
      (PDF, 27 Kbytes)
    • J.M. Porta, J.J. Verbeek, and B.J.A. Kröse.Active appearance-based robot localization using stereo vision.Autonomous Robots, 18(1):59-80, 2005.(PDF, 2262 Kbytes)
    • J.M. Porta and
      B.J.A Kröse.
      Appearance-based concurrent map building and localization.
      Robotics and Autonomous Systems, 54(2):159-164, 2005.
      ISBN 0921-8890.
      (PDF, 1120 Kbytes)
    • J.J. Verbeek, N.Vlassis, and B.J.A. Kröse.Self-organizing mixture models.Neurocomputing, 63:99-123, 2005.(PDF, 859 Kbytes)
    • W.Zajdel and
      B.J.A. Kröse.
      A sequential bayesian algorithm for surveillance with non-overlapping
      cameras
      .
      Int. Journal of Pattern Recognition and Artificial Intelligence,
      19(8):977-996, 2005.
      (PDF, 568 Kbytes)
    • W.Zajdel, N.Vlassis,
      and B.J.A Kröse.
      Bayesian methods for tracking and localization.
      In E.Aarts, J.Korts, and W.Verhaegh, editors, Intelligent
      Algorithms
      , pages 243-258. Kluwer Academic Publishers, 2005.
      (PDF, 166 Kbytes)
    • W.Zajdel, Z.Zivkovic,
      and B.J.A. Kröse.
      Keeping track of humans: have I seen this person before?.
      In Proc. of Int. Conference on Robotics and Automation (ICRA),
      pages 2093-2098, 2005.
      (Gzipped PostScript, 6 pages, 1179 Kbytes)
      (PDF, 1517 Kbytes)
    • Z.Zivkovic
      and B.J.A. Kröse.
      On matching interest regions using local descriptors – can an information
      theoretic approach help?
      .
      In Proc. British Machine Vision Conference, pages 50-58, 2005.
      (PDF, 241 Kbytes)
    • Z.Zivkovic,
      B.Bakker, and B.J.A. Kröse.Hierarchical map building using visual landmarks and geometric
      constraints
      .
      In Proc. IEEE/RSJ International Conference on Intelligent Robots and
      Systems
      , pages 7-12, 2005.
      (PDF, 550 Kbytes)

    2004

    • B.Kröse,
      R.Bunschoten, S.ten Hagen, B.Terwijn, and N.Vlassis.
      Household robots look and learn.
      IEEE Robotics and Automation Magazine, 11(4):45-52, December
      2004.
    • B.J.A. Kröse,
      N.Vlassis, and W.Zajdel.
      Bayesian methods for tracking and localization.
      In Proc. of Philips Symposium On Intelligent Algorithms, (SOIA),
      pages 27-38, 2004.
      (Gzipped PostScript, 12 pages, 184 Kbytes)
      (PDF, 167 Kbytes)
    • J.M. Porta and
      B.J.A. Kröse.
      Appearance-based concurrent map building and localization.
      In F.C.A. Groen, editor, International Conference on Intelligent
      Autonomous Systems, IAS’04
      , pages 1022-1029. IOS Press, March 2004.
      ISBN 1-58603-414-6.
    • J.M. Porta and
      B.J.A. Kröse.
      Appearance-based concurrent map building and localization using a
      multi-hypotheses tracker.
      .
      In Proc.IEEE/RSJ International Conference on Intelligent Robots and
      Systems
      , pages 3424-3429, Sendai, Japan, 2004. IEEE Press.
      (PDF, 156 Kbytes)
    • Martijn Reuvers,
      Richard Kleihorst, Harry Broers, and Ben Kröse.
      A smart camera for face recognition.
      In Proceedings of SPS-2004, 2004.
      (PDF, 225 Kbytes)
    • S.H.G. ten
      Hagen and B.J.A. Kröse.
      Learning to understand tasks for mobile robots.
      In Proc. of the IEEE Int. Conf. on System, Man and Cybernetics,
      The Hague, The Netherlands, October 2004.
      To Appear.
      (Gzipped PostScript, 6 pages, 467 Kbytes)
      (PDF, 448 Kbytes)
    • J.M. Terwijn,
      B.Porta and B.J.A. Kröse.
      A particle filter to estimate non-markovian states.
      In F.C.A. Groen, editor, International Conference on Intelligent
      Autonomous Systems, IAS’04
      , pages 1062-1069. IOS Press, March 2004.
      ISBN 1-58603-414-6.
    • W.Zajdel, A.T. Cemgil,
      and B.Kröse.
      Online multicamera tracking with a switching state-space model.
      In Proc. of IEEE International Conference on Pattern Recognition
      (ICPR)
      , pages IV:339-343, Cambridge, UK, 2004.
      (PDF, 175 Kbytes)
    • Z.Zivkovic
      and B.Kröse.
      An EM-like algorithm for color-histogram-based object tracking.
      In IEEE Conference on Computer Vision and Pattern Recognition,
      June 2004.
      To appear.
      (PDF, 372 Kbytes)
    • Z.Zivkovic
      and B.Kröse.
      A probabilistic model for an EM-like object tracking algorithm using
      color-histograms
      .
      In 6th IEEE International Workshop on Performance Evaluation of Tracking
      and Surveillance (in connection with ECCV2004)
      , May 2004.
      To appear.
      (PDF, 171 Kbytes)

    2003

    • R.Bunschoten and B.Kröse.
      Robust scene reconstruction from an omnidirectional vision system.
      IEEE Transactions on Robotics and Automation, 19(2):351-357,
      2003.
      (PDF, 886 Kbytes)
    • >Roland
      Bunschoten and Ben Kröse.
      Visual odometry from an omnidirectional vision system.
      In Proceedings of the International Conference on Robotics and Automation
      ICRA’03
      , pages 577-583, Taipei, Taiwan, 2003.
      ISBN 0-7803-7737-0.
    • B.J.A. Kröse,
      J.M. Porta, K.Crucq, A.J.N. van Breemen, M.Nuttin, and E.Demeester.
      Lino, the user-interface robot.
      In E.Aarts, R.Collier, E.van Loenen, and B.D. Ruyter, editors,
      Proceedings of the First European Symposium on Ambience Intelligence
      (EUSAI)
      , pages 264-274, Eindhoven, The Netherlands, November 2003.
      Springer.
      ISBN 3-540-20418-0.
      (PDF, 7004 Kbytes)
    • J.M. Porta and
      B.J.A. Kröse.
      Vision-based localization for mobile platforms.
      In E.Aarts, R.Collier, E.van Loenen, and B.D. Ruyter, editors,
      Proceedings of the First European Symposium on Ambience Intelligence
      (EUSAI)
      , pages 208-219, Eindhoven, The Netherlands, November 2003.
      Springer.
      ISBN 3-540-20418-0.
      (PDF, 2051 Kbytes)
    • JosepM. Porta
      and Ben Kröse.
      On the use of disparity maps for robust robot localization under different
      illumination conditions
      .
      In A.T. deAlmeida and U.Nunes, editors, Proceedings of the 11th
      International Conference on Advanced Robotics, ICAR’03
      , pages
      124-129, Coimbra, Portugal, June 30-July 3 2003. IEEE Press.
      ISBN 972-96889-9-0.
      (Gzipped PostScript, 6 pages, 345 Kbytes)
      (PDF, 988 Kbytes)
    • J.M. Porta, J.J.
      Verbeek, and B.J.A. Kröse.
      Enhancing appearance-based robot localization using sparse disparity
      maps
      .
      In C.S.G. Lee and J.Yuh, editors, Proc.IEEE/RSJ International
      Conference on Intelligent Robots and Systems
      , pages 980-985, Las
      Vegas, USA, October 2003. IEEE Press.
      ISBN 0-7803-7861-X.
      (PDF, 253 Kbytes)
    • JosepM. Porta, Bas
      Terwijn, and Ben Kröse.
      Efficient entropy-based action selection for appearance-based robot
      localization
      .
      In Proceedings of the International Conference on Robotics and Automation
      ICRA’03
      , pages 2842-2847, Taipei, Taiwan, 2003.
      ISBN 0-7803-7737-0.
      (PDF, 114 Kbytes)
    • Stephan ten
      Hagen and Ben Kröse.
      Neural Q-learning.
      Neural Computing & Applications, 12(2):81-88, November 2003.
      ISSN: 0941-0643 (Paper) 1433-3058 (Online).
      (Gzipped PostScript, 13 pages, 163 Kbytes)
      (PDF, 248 Kbytes)
    • S.H.G. ten
      Hagen and B.J.A. Kröse.
      Learning to navigate using a lazy map.
      In A.T. deAlmeida and U.Nunes, editors, Proceedings of the 11th
      International Conference on Advanced Robotics, ICAR’03
      , pages
      299-304, Coimbra, Portugal, June 30-July 3 2003.
      (Gzipped PostScript, 6 pages, 157 Kbytes)
      (PDF, 119 Kbytes)
    • A.J.N van
      Breemen, K.Crucq, B.J.A Kröse, M.Nuttin, J.M. Porta, and E.Demeester.A user-interface robot for ambient intelligent environments.
      In P.Fiorini, editor, Proceedings of the 1st International Workshop on
      Advances in Service Robotics, ASER’03
      , pages 132-139, Bardolino,
      Italy, 2003. Fraunhofer IRB Verlag.
      (Gzipped PostScript, 8 pages, 5288 Kbytes)
      (PDF, 3287 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.J.A. Kröse.
      Efficient greedy learning of Gaussian mixture models.
      Neural Computation, 15(2):469-485, 2003.
      (PDF, 505 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.J.A. Kröse.
      Non-linear feature extraction by the coordination of mixture models.
      In S.Vassiliadis, L.M.J. Florack, J.W.J. Heijnsdijk, and A.vander Steen,
      editors, Proc. 8th Ann. Conf. of the Advanced School for Computing and
      Imaging (ASCI)
      , pages 287-293, Heijen, The Netherlands, June 2003.
      ASCI.
      (PDF, 1331 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.J.A. Kröse.
      Self-organization by optimizing free-energy.
      In M.Verleysen, editor, Proc. of European Symposium on Artificial Neural
      Networks
      , pages 125-130. D-side, Evere, Belgium, 2003.
      (PDF, 184 Kbytes)
    • A.H.G. Versluis,
      B.J.F Driessen, J.A. van Woerden, and B.J.A. Kröse.
      Enhancing the usability of the MANUS manipulator by using visual
      servoing
      .
      In Proceedings of International Conference on Rehabilitation Robotics,
      ICORR 2003
      , pages 43-46, KAIST, Daejon, South Korea, 22-25 April
      2003.
    • Wojciech
      Zajdel and Ben Kröse.
      Approximate learning and inference for tracking with non-overlapping
      cameras
      .
      In M.H. Hamza, editor, Proc. of the IASTED Int. Conf. on Artificial
      Intelligence and Applications
      , pages 70-75. ACTA Press, Calgary,
      Canada, 2003.
      (PDF, 125 Kbytes)
    • Wojciech
      Zajdel and Ben Kröse.
      Gaussian mixture model for multi-sensor tracking.
      In T.Heskes, P.Lucas, L.Vuurpijl, and W.Wiegerinck, editors,
      Proceedings of the 15th Dutch-Belgian Artificial Intelligence
      Conference, BNAIC’03
      , pages 371-378, Nijmegen, The Netherlands,
      October 2003. Elsevier.
      (PDF, 200 Kbytes)

    2002

    • R.Bunschoten and B.Kröse.
      3-D scene reconstruction from cylindrical panoramic images.
      Robotics and Autonomous Systems (special issue), 41(2/3):111-118,
      November 2002.
      (PDF, 225 Kbytes)
    • B.J.A. Kröse,
      N.Vlassis, and R.Bunschoten.
      Omnidirectional vision for appearance-based robot localization.
      In G.D. Hagar, H.I. Cristensen, H.Bunke, and R.Klein, editors, Sensor
      Based Intelligent Robots: International Workshop, Dagstuhl Castle, Germany,
      October 2000, Selected Revised Papers
      , number 2238 in Lecture Notes in
      Computer Science, pages 39-50. Springer, 2002.
      (PDF, 730 Kbytes)
    • S.H.G. ten
      Hagen and B.J.A. Kröse.
      Towards global consistent pose estimation from images.
      In R.Siegwart and C.Laugier, editors, Proc.IEEE/RSJ International
      Conference on Intelligent Robots and Systems
      , pages 466-471,
      Lausanne,Switzerland, September 2002. Omnipress.
      (Gzipped PostScript, 6 pages, 317 Kbytes)
      (PDF, 176 Kbytes)
    • S.H.G. ten
      Hagen and B.J.A. Kröse.
      Trajectory reconstruction for self-localization and map building.
      In W.R. Hamel and A.A. Maciejewski, editors, Proc. IEEE Int. Conf. on
      Robotics and Automation
      , pages 1796-1801, Washington D.C., USA, May
      2002. Omnipress.
      (Gzipped PostScript, 6 pages, 168 Kbytes)
      (PDF, 143 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.Kröse.
      A k-segments algorithm for finding principal curves.
      Pattern Recognition Letters, 23(8):1009-1017, 2002.
      (Gzipped PostScript, 12 pages, 97 Kbytes)
      (PDF, 149 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.J.A. Kröse.
      Coordinating Principal Component Analyzers.
      In J.R. Dorronsoro, editor, Proceedings of International Conference on
      Artificial Neural Networks
      , Lecture Notes in Computer Science,
      pages 914-919, Madrid, Spain, August 2002. Springer.
      (PDF, 251 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.J.A. Kröse.
      Fast non-linear dimensionality reduction using topology preserving
      networks
      .
      In M.Verleysen, editor, Proc. of European Symposium on Artificial Neural
      Networks
      , pages 193-198. D-side, Evere, Belgium, 2002.
      (PDF, 167 Kbytes)
    • J.J. Verbeek,
      N.Vlassis, and B.J.A. Kröse.
      Locally linear generative topographic mapping.
      In M.Wiering, editor, Benelearn 2002: Proceedings of the Twelfth
      Belgian-Dutch Conference on Machine Learning
      , Utrecht, The
      Netherlands, December 2002.
      (PDF, 158 Kbytes)
    • N.Vlassis,
      Y.Motomura, and B.Kröse.
      Supervised dimension reduction of intrinsically low-dimensional data.
      Neural Computation, 14(1):191-215, January 2002.
      (Gzipped PostScript, 22 pages, 331 Kbytes)
    • N.Vlassis,
      B.Terwijn, and B.Kröse.
      Auxiliary particle filter robot localization from high-dimensional sensor
      observations
      .
      In W.R. Hamel and A.A. Maciejewski, editors, Proc. IEEE Int. Conf. on
      Robotics and Automation
      , pages 7-12, Washington D.C., USA, May 2002.
      Omnipress.
      (Gzipped PostScript, 6 pages, 218 Kbytes)
      (PDF, 178 Kbytes)
    • W.Zajdel and
      B.Kröse.
      Bayesian network for multiple hypothesis tracking.
      In H.Blockeel and M.Denecker, editors, Proceedings of the 14th
      Dutch-Belgian Artificial Intelligence Conference, BNAIC’02
      , pages
      379-386, Leuven, Belgium, October 2002.
      (Gzipped PostScript, 8 pages, 52 Kbytes)
      (PDF, 74 Kbytes)


      2001


      H. Asoh, N. Vlassis, Y. Motomura, F. Asano,
      I. Hara, S. Hayamizu, K. Itou, T. Kurita, T. Matsui, R. Bunschoten, and Ben
      Kröse. Jijo-2: An office robot that communicates and learns.
      IEEE Intelligent Systems, 16(5):46-55, Sep/Oct 2001. (PDF,
      1107 Kbytes)


      R. Bunschoten and B. Kröse. 3-d
      scene reconstruction from cylindrical panoramic images
      . In Proceedings
      of the 9th International Symposium on Intelligent Robotic Systems (SIRS’2001)
      ,
      pages 199-205, LAAS-CNRS, Toulouse, France, July 2001. (PDF,
      220 Kbytes)


      R. Bunschoten and B. Kröse. 3-d
      scene reconstruction from multiple panoramic images
      . In Proceedings
      of 7th annual conference of the Advanced School for Computing and Imaging
      (ASCI 2001)
      , pages 49-54, Heijen, The Netherlands, May 2001. ASCI. (PDF,
      141 Kbytes)


      R. Bunschoten and B. Kröse. Range
      estimation from a pair of omnidirectional images
      . In Proc. IEEE Int.
      Conf. on Robotics and Automation
      , pages 1174-1179, Seoul, Korea, May 2001.


      F.C.A. Groen, W. van der Hoek, P. Jonker,
      B. Kröse, H. Spoelder, and S. Stramigioli. Robocup european championship:
      Report on the Amsterdam 2000 event
      . Robotics and Autonomous Systems,
      36(2-3):59-66, August 2001.


      B.J.A. Kröse, N. Vlassis, R. Bunschoten,
      and Y. Motomura. A probabilistic model for appearance-based robot localization.
      Image and Vision Computing, 19(6):381-391, April 2001. (Gzipped
      PostScript
      , 17 pages, 604 Kbytes) (PDF,
      1074 Kbytes)


      Stephan ten Hagen. Continuous State
      Space Q-Learning for Control of Nonlinear Systems
      . PhD thesis, Computer
      Science Institute, University of Amsterdam, The Netherlands, February 2001.
      (Gzipped
      PostScript
      , 128 pages, 1059 Kbytes) (PDF,
      1729 Kbytes)


      J.J. Verbeek, N. Vlassis, and B. Kröse.
      Efficient greedy learning of Gaussian mixtures. In Proc.
      13th Belgian-Dutch Conf. on Artificial Intelligence
      , Amsterdam, The Netherlands,
      October 2001.


      J.J. Verbeek, N. Vlassis, and B. Kröse.
      Greedy Gaussian mixture learning for texture segmentation.
      In A. Leonardis and H. Bischof, editors, ICANN’01, Workshop on Kernel
      and Subspace Methods for Computer Vision
      , pages 37-46, Vienna, Austria,
      August 2001.


      J.J. Verbeek, N. Vlassis, and B. Kröse.
      A soft k-segments algorithm for principal curves. In Proc.
      Int. Conf. on Artificial Neural Networks
      , pages 450-456, Vienna, Austria,
      August 2001. (Gzipped
      PostScript
      , 7 pages, 80 Kbytes)


      N. Vlassis, R. Bunschoten, and B. Kröse.
      Learning task-relevant features from robot data. In Proc.
      IEEE Int. Conf. on Robotics and Automation
      , pages 499-504, Seoul, Korea,
      May 2001. (Gzipped
      PostScript
      , 6 pages, 200 Kbytes)


      N. Vlassis, Y. Motomura, and Ben Kröse.
      Supervised dimension reduction of intrinsically low-dimensional
      data
      . Neural Computation, 14:1-25, 2001. To appear. (Gzipped
      PostScript
      , 22 pages, 331 Kbytes)


      2000


      PICTURE
      Kröse,B.J.A. , R. van den Bogaard and N. Hietbrink (2000)

      “Programming robots is fun: Robocup Jr. 2000”
      van den Bosch and Weigand (ed.), Proceedings of the Twelfth Belgium-Netherlands
      AI Conference BNAIC’00, pp 29-36, 2000
      , pp , Gzipped
      postscript
      281Kb
      PDF 308Kb


      PICTURE
      Kröse,B.J.A. (2000)

      “An efficient representation of the robot’s
      environment” Proc. Intelligent Autonomous Systems 6, Venice, Italy,
      IOS press, ISBN 90 51993986, pp 589-595, Gzipped postscript 110Kb PDF
      362Kb


      PICTURE
      Kröse,B.J.A., Vlassis, N., Bunschoten,
      R and Motomura, Y.
      (2000)

      “Feature selection for appearance-based robot
      localization” Proceedings 2000 RWC Symposium, RWC Technical Report
      (TR-99-002) Gzipped postscript 334Kb


      PICTURE
      Kröse,B.J.A. A. Dev and F.C.A. Groen (2000)

      “ Heading Direction of a Mobile Robot from
      the Optical~Flow” Image and Vision Computing Journal, vol.18 nr. 5,
      pp. 415-424 Gzipped postscript 1.9Mb


      PICTURE
      Portegies Zwart, Joris and Kröse, Ben(2000)

      “ Constrained Mixture Modeling of Intrinsically
      Low-Dimensional Distributions,” 15th International Conference on Pattern
      Recognition, Volume 2: Pattern Recognition and Neural Networks,
      (Sanfeliu,
      A. and Villanueva, J.J. and Vanrell, M. and Alquézar, R. and Jain,
      A.K. and Kittler, J., ed.), IEEE,pp. 610-613 Postscript available from
      Joris’
      web page


      PICTURE
      Wiering, M., Kröse,B.J.A. and F.C.A. Groen (2000)

      “Learning in Multi-Agent Systems” SubmittedGzipped
      postscript
      134Kb


      PICTURE
      Vlassis, N., Motomura, Y. and Kröse,B.J.A. (2000)

      “Supervised linear feature extraction for mobile
      robot localization” Proceedings of the IEEE International Conference
      on Robotics and Automation
      Gzipped postscript 239Kb


      1999


      PICTURE
      Kröse,B.J.A., Bunschoten,R., N. Vlassis,
      Y. Motomura
      (1999)

      “ Appearance based robot localization” IJCAI-99
      Workshop Adaptive Spatial Representations of Dynamic Environments, Stockholm,
      Sweden
      Gzipped postscript 0.5Mb


      PICTURE
      Kröse,B.J.A. and Bunschoten,R. (1999)

      “ Probabilistic localization by appearance
      models and active vision” Proceedings of the IEEE International Conference
      on Robotics and Automation, pp 2255-2260
      Gzipped postscript
      file:
      300kb


      PICTURE
      Y. Motomura, N. Vlassis, B. Kröse (1999)

      “ Probabilistic Robot Localization and Situated
      Feature Focusing” Proc. SMC’99, IEEE Int. Conf. on Systems, Man, and
      Cybernetics, Tokyo, Japan, Oct 1999.


      PICTURE
      Y. Motomura, N. Vlassis, B. Kröse (1999)

      “ Environment Modeling via PCA Regression and
      Situated Feature Focusing” Special Interest Group on Mathematical modeling
      and Problem Solving of Information Processing Society of JAPAN, May 1999.


      PICTURE
      N. Vlassis, Y. Motomura, B. Kröse (1999)

      “ An information-theoretic localization criterion
      for robot map building” Proc. ACAI’99, Int. Conf. on Machine Learning
      and Applications Chania, Greece, Jul 1999


      PICTURE
      N. Vlassis, B. Kröse (1999)

      “ Mixture Conditional Density Estimation with
      the EM Algorithm” Proc. ICANN’99, 9th Int. Conf. on Artificial Neural
      Networks, Edinburgh, Scotland, Sep 1999.


      PICTURE
      N. Vlassis, B. Kröse (1999)

      “ Robot Environment Modeling via Principal
      Component Regression” Proc. IROS’99, IEEE/RSJ Int. Conf. on Intelligent
      Robots and Systems, Kyongju, Korea, Oct 1999.


      1998


      PICTURE
      Corten, E. and
      Dorst, L. and Krose B.
      (1998)

      “ The design of OASIS: Open Architecture for
      Simulations with Intelligent Systems,” Proc ESM’98, Manchester June
      16-19 1998, SCS Publication, ISBN 1- 56555-148-6,
      (Zobel, R. and Moeller,
      D, ed.), pp. 455-459


      PICTURE
      Corten, E. and Dorst, L. and Krose,
      B.
      (1998)

      “ OASIS: Open Architecture for Simulations
      with Intelligent Systems,” Proc IAS-5, Sapporo June 2-4 1998, IOS press,
      ISBN 90 51993986,
      (Kakazu, Y. and Wada, M. and Sato, T., ed.), pp. 6-12


      PICTURE
      Dev, A. and Kröse, B.J.A. and Groen,
      F.C.A.
      (1998)

      “ Where are you driving to? Heading direction
      for a Mobile Robot from Optic Flow ,” Proceedings of the IEEE International
      Conference on Robotics and Automation,
      pp. 1578-1583

      Postscript file: click
      here to get 396 Kb
      Abstract: If a camera moves on a
      straight line, the optic flow field is a diverging vector field, of which
      the singularity is called “focus of expansion”. An object which is seen in
      this FOE is located on the future path of the camera. If the camera is also
      rotating, the future path is no longer a point in the image domain, but a
      line. All objects which are on the future path (and thus will cause collisions)
      are projected on this line. However, not necessary the reverse is true: not
      all points on the line are collision points. In this paper we derive how
      the optic flow can be used to compute which points in the image are projections
      of collision points.

      PICTURE
      Dev, A. and Krose,
      B.J.A and Groen, F.C.A.
      (1998)

      “ Predicting the future path from optic flow.,”
      Proc. 1998 RWC Symposium, Tokyo June 9-10 1998, RWC Technical Report
      TR-98001,
      pp. 265-270


      PICTURE
      Hagen, Stephan ten and Kröse, Ben
      (1998)

      “ Reinforcement learning for realistic manufacturing
      processes,” CONALD 98, Conference on Automated Learning and Discovery,
      Carnegie Mellon University, Pittsburgh, PA,

      Postscript file: click
      here to get 67 Kb
      Abstract: This manuscript is a submission
      to the workshop “Machine Learning and Reinforcement Learning for Manufacturing”.
      It introduces and positions our part in a project and motivates our approach
      with respect to reinforcement learning and manufacturing processes. In an
      “extended appendix” some additional information will be given about our
      problem domain and preliminary results. Our main issue is that advances in
      algorithms and theory should not be scaled up to bigger problems, but to
      more realistic problems. Realistic in the sense that the problem is formulated
      with the problems of existing manufacturing processes in mind.

      PICTURE
      Hagen, S.H.G ten
      and Kröse, B.J.A.
      (1998)

      “ Pseudo-Parametric Q-Learning using Feedforward
      Neural Networks,” ICANN’98, Proceedings of the International Conference
      on Artificial Neural Networks,
      (Niklasson, L., Bodén, M. and
      Ziemke, T., ed.), Springer-Verlag, pp. 449-454

      Postscript file: click
      here to get 62 Kb
      Abstract: In this paper we focus
      on Q-learning in domains with continuous state and action spaces. We discuss
      how Q-learning relates to System Identification (SI) methods for Linear Quadratic
      Regulation (LQR) and show how the methods compare on linear systems. We also
      study the use of a feedforward network as a nonlinear function approximator
      for the Q-function and introduce the the concept of Pseudo-Parametric Q-Learning
      (PPQL). In the PPQL framework the feedforward network is implemented such,
      that the results can be interpreted in terms of LQR conditions. Experiments
      show that it performs well, but does not necessarily converge to a stable
      solution. The LQR interpretation indicates the origin of that problem.

      PICTURE
      Hagen, S.H.G. ten
      and Kröse, B.J.A.
      (October 1998)

      “ Linear Quadratic Regulation using Reinforcement
      Learning,” Proc. of the 8th Belgian-Dutch Conf. on Machine Learning,,
      (F. Verdenius and W. van den Broek, ed.), pp. 39-46

      Postscript file: click
      here to get 82 Kb
      Abstract: In this paper we describe
      a possible way to mak e reinforcement learning more applicable in the context
      of industrial manufactur ing processes. We achieve this by formulating the
      optimization task in the linear quadratic reg ulation framework, for which
      a conventional control theoretic solution exist. By rewriting the Q-learning
      approach into a linear least squares approximation p roblem, we can make a
      fair comparison between the resulting approximation and th at of the conventional
      system identification approach. Our experiment shows that the conventional
      approach performs slightly better. Also we can show that the amount of exploration
      noise, added during the generati on of data, plays a crucial role in the outcome
      of both approaches.

      PICTURE
      Kröse, B.J.A
      (1998)

      “ Environment learning and localization in
      `sensor-space’,” Proc. of the 10th Netherlands/Belgium Conf. on
      Artificial Intelligen ce,
      pp. 229-239

      Postscript file: click
      here
      Abstract: For navigation to a desired
      state, a mobile rob ot needs some sort of global information about the environment
      it is operating i n. Usually this is provided in the form of a map, giving
      locations of objects and f ree space in the working space of the robot.
      Such a map can be provided by the programmer or learned by the system itself.
      In this paper an approach is described where the global information is not
      cast in a model of the geometry of the envir onment but in a model of all
      sensory data of the robot. Experimental results are presented.

      1997


      PICTURE
      Dev, A. and Kröse,
      B.J.A. and Groen, F.C.A.
      (1997)

      “ Confidence measures for Image Motion Estimation,”
      Proceedings 1997 RWC Symposium, RWC Technical Report TR – 96001,
      pp. 199-206

      Postscript file: click
      here to get 295 Kb
      Abstract: Estimation of image motion,
      also known as the optic flow, from a sequence of images is known to be difficult.
      This is due to: the sensitivity of the image motion model to noise (the
      derivative property), the limited observability of the image motion from
      the luminance (the aperture problem), and, the non-validity of the optic
      flow constraint (the assumption of intensity conservation). In this paper
      we analyze measures that assign a confidence value to the estimated image
      motion: the sensitivity of the model to noise, the validity of the model
      and the estimated variance of the image motion. Experiments show that selection
      of image motion vectors based on these measures dramatically improve the
      estimates of the image motion while keeping as much image motion vectors
      as possible. We conclude that the proposed estimated variance of the image
      motion optimizes this trade-off.

      PICTURE
      Dev, A. and Kröse,
      B.J.A. and Groen, F.C.A.
      (1997)

      “ Navigation of a mobile robot on the temporal
      development of the optic flow,” Proceedings IROS’97, IEEE , pp.
      558-563

      Postscript file: click
      here to get 450 Kb
      Abstract: The robot navigation task
      presented in this paper is to drive through the center of a corridor, based
      on a sequence of images from an on-board camera. Our measurements of the
      system state, the distance to the wall and orientation of the wall, are derived
      from the optic flow. Whereas the structure of the environment is usually
      computed from the spatial derivatives of the optic flow, we use the structure
      contained in the temporal derivatives of the optic flow to compute the environment
      structure and hence the system state. The algorithm is used to control a
      `remote brain’ robot and results on the accuracy of the state estimates
      are presented.

      PICTURE
      Hagen,S.H.G. ten
      and Kröse, B.J.A.
      (1997)

      “ Generalizing in TD($\lambda$) learning,”
      Procedings of the third Joint Conference of of Information Sciences,
      Durham, NC, USA,
      (Wang,P.P, ed.), pp. 319-322

      Postscript file: click
      here to get 52 Kb
      Abstract: Convergence of TD($\lambda$)
      with radial base function network.

      PICTURE
      Hagen, S.H.G. ten
      and Kröse, B.J.A.
      (October 1997)

      “ A Short Introduction to Reinforcement Learning,”
      Proc. of the 7th Belgian-Dutch Conf. on Machine Learning, (W.
      Daelemans and P. Flach and A. van den Bosch, ed.), pp. 7-12

      Postscript file: click
      here to get 69 Kb
      Abstract: This introduction is meant
      for readers with no knowledge about reinforcement learning. It presents the
      basic framework and introduce the basic terminology. We hope that this will
      make it easier to read other reinforcement learning literature. Pointers to
      more tutorial sources will be given at the end.

      PICTURE
      Hagen, S.H.G. ten
      and Kröse, B.J.A.
      (October 1997)

      “ Towards a Reactive Critic,” Proc. of
      the 7th Belgian-Dutch Conf. on Machine Learning,,
      (W. Daelemans and
      P. Flach and A. van den Bosch, ed.), pp. 49-58

      Postscript file: click
      here to get 86 Kb
      Abstract: In this paper we propose
      a reactive critic, that is able to respond to changing situations. We will
      explain why this is useful in reinforcement learning, where the critic is
      used to improve the control strategy. We take a problem for which we can derive
      the solution analytically. This enables us to investigate the relation between
      the parameters and the resulting approximations of the critic. We will also
      demonstrate how the reactive critic reponds to changing situations.

      PICTURE
      Kröse, B.J.A.
      and Dam, J.W.M. van
      (1997)

      “ Neural Vehicles,” Neural Systems for
      Robotics,
      (Omid Omidvar and P.P. van der Smagt, ed.), Academic Press,
      pp. 271-296

      Postscript file: click
      here to get 76 Kb
      Abstract: A review is given on the
      use of neural networks for mobile robots and autonomous vehicles. We focus
      on neural methods for navigation, making a distinction between sensor-based
      `reactive’ navigation and planned navigation methods.

      PICTURE
      Kröse,
      B.J.A. and Dev, A. and Benavent, X. and Groen, F.C.A.
      (1997)

      “ Visual Navigation on Optic Flow,” Proceedings
      1997 RWC Symposium, RWC Technical Report TR – 96001,
      pp. 89-95

      Postscript file: click
      here to get 450 Kb
      Abstract: We describe a remote brain
      mobile robot based on off-the-shelve components. The navigation task presented
      in this paper is to drive through the center of a corridor, based on a sequence
      of images from an on-board camera. A simple control scheme is presented.
      Our measurements of the system state, the distance to the wall and orientation
      of the wall, are derived from the optic flow. Whereas this structure of
      the environment is usually computed from the spatial structure of the optic
      flow, i.e. the spatial derivatives of the optic flow, for robustness reasons
      we use the structure contained in the temporal derivatives of the optic flow
      to compute the environment structure and hence the system state.

      PICTURE
      Stomp, P. and
      Wortel, M.P. and Kröse, B.J.A. and Stuurman, F.
      (1997)

      “ Neural Networks for the analysis of flight-booking
      profiles,” Neural Networks, Best Practice in Europe, pp. 206-209

      Postscript file: click
      here to get 79 Kb
      Abstract: Because of the huge amount
      of data which is available nowadays, the current manager or decision maker
      needs intelligent data analysis tools. Those tools must be able to visualize
      the data, to cluster the data or to make predictions based on the data.
      In this paper we describe how neural networks have been used for the analysis
      of flight booking profiles at KLM Royal Dutch Airlines.
      Note: Presented at “SNN’97, Europe’s
      best neural networks practice’, Amsterdam, 22 May 1997.

      PICTURE
      Yakali, H.H. and
      Kröse, B.J.A. and Dorst, L.
      (1997)

      “ Vision-Based 6-dof Robot End-effector Positioning
      Using Neural Networks,” Proceedings 1997 RWC Symposium, RWC Technical
      Report TR – 96001,
      pp. 191-198

      Postscript file: click
      here to get 100 Kb
      Abstract: We present a method for
      vision-based model-free positioning of a 6-degree-of-freedom robot end-effector
      with respect to a planar target object using a feed-forward neural network.
      We investigate the necessary conditions under which a neural network can
      learn the mapping from feature domain to actuator domain. After satisfying
      these conditions, a neural network is used to learn this mapping. We consider
      only planar objects as target and their binary images. Moment-based image
      descriptors are used to represent the image in the feature domain. Simulation
      results are also presented.

      PICTURE
      Yakali, H.H. and
      Dorst, L. and Kröse, B.J.A.
      (1997)

      “ Pose characterization by independent moment-based
      image features of planar objects,” RWCP Novel Functions: SNN Laboratory,
      Faculty of Mathematics and Computer Science, University of Amsterdam

      Postscript file: click
      here to get 65 Kb
      Abstract: For a unique characterization
      of the relative position between a 2-D planar object (target) and a camera,
      the following two mappings have to be single-valued: mapping from the relative
      position to the image plane and from the image plane to the feature domain.
      We consider only white planar targets located in a black background and
      designed a special target which allows a unique perspective from any relative
      position. From the image of this target, the 6 relative position and orientation
      parameters can be characterized by means of 6 independent features. We use
      moments to extract these features and choose the proper representation to
      make them independent.

      1996


      PICTURE
      Dam, J.W.M. van
      and Kröse, B.J.A. and Groen, F.C.A.
      (Dec. 8-11, 1996)

      “ Adaptive Sensor Models,” 1996 IEEE/SICE/RSJ
      Intr. Conf. on Multisensor Fusion and Integration for Intelligent Systems,
      Washington D.C,
      pp. 705-712

      Postscript file: click
      here to get 143 Kb
      Keywords: learning sensor models,
      neural networks, sensor fusion, occupancy grids
      Abstract: In this paper we consider
      the conversion of sensor data to a probabilistic representation of the environment
      (occupancy grid). We introduce a neural network which learns these conversions.
      The conversion of sensor data remains adaptive to changes in either the sensor
      or its environment. To place this in a broader context, we describe the
      architecture of our Sensor Data Fusion system in which these conversions
      are applied. We also introduce the PDOP: a rule for fusing occupancy grids
      in this system.

      PICTURE
      Dam, J.W.M. van
      and Kröse, B.J.A. and Groen, F.C.A.
      (1996)

      “ Neural Network Applications in Sensor Fusion
      for an Autonomous Mobile Robot,” Reasoning with Uncertainty in Robotics,
      (Dorst, L. and Lambalgen, M. van and Voorbraak, F., ed.), Springer, pp.
      263-277

      Postscript file: click
      here to get 112 Kb
      Keywords: learning sensor models,
      neural networks, sensor fusion, occupancy grids
      Abstract: Key issue in the design
      of a sensor data fusion system is the conversion of sensor measurements to
      an internal representation. In this article, we identify the problems with
      traditional conversion methods and we introduce a neural network which learns
      how to convert such measurements.

      PICTURE
      Schram, G. and
      Kröse, B.J.A. and Babuska, R. and Krijgsman, A.J.
      (1996)

      “ Neurocontrol by Reinforcement Learning,”
      Journal a (Journal on Automatic Control), Special Issue on Neurocontrol
      37 (3), pp. 59-64

      Postscript file: click
      here to get 103 Kb
      Abstract: Reinforcement learning
      (RL) is a model-free tuning and adaptation method for control of dynamic systems.
      Contrary to supervised learning, based usually on gradient descent techniques,
      RL does not require any model or sensitivity function of the process. Hence,
      RL can be applied to systems that are poorly understood, uncertain, nonlinear
      or for other reasons untractable with conventional methods. In reinforcement
      learning, the overall controller performance is evaluated by a scalar measure,
      called reinforcement. Depending on the type of the control task, reinforcement
      may represent an evaluation of the most recent control action or, more often,
      of an entire sequence of past control moves. In the latter case, the RL system
      learns how to predict the outcome of each individual control action. This
      prediction is then used to adjust the parameters of the controller. The mathematical
      background of RL is closely related to optimal control and dynamic programming.
      This paper gives a comprehensive overview of the RL methods and presents
      an application to the attitude control of a satellite. Some well known applications
      from the literature are reviewed as well.

      PICTURE
      Schram, G. and
      Linden, F.X. van der and Kröse, B.J.A. and Groen, F.C.A.
      (1996)

      “ Visual Tracking of Moving Objects using a
      Neural Network Controller,” Robotics and Autonomous Systems, pp.
      293-299

      Postscript file: click
      here to get 114 Kb
      Abstract: For a target tracking
      task, the hand-held camera of the anthropomorphic OSCAR-robot manipulator
      has to track an object which moves arbitrarily on a table. The desired camera-joint
      mapping is approximated by a feedforward neural network. Through the use
      of time derivatives of the position of the object and of the manipulator,
      the controller can inherently predict the next position of the moving target
      object. In this paper several `anticipative’ controllers are described, and
      successfully applied to track a moving object.

      1995


      PICTURE
      Dev, A. and Kröse,
      B.J.A. and Groen, F.C.A.
      (Sep. 1995)

      “ Learning Structure from Motion: How to Represent
      Two-Valued Functions,” Proceedings of the 3rd SNN Symposium on Neural
      Networks,
      (Kappen, B. and Gielen, S., ed.), Foundation for Neural Networks,
      Nijmegen, pp. 121-128

      Postscript file: click
      here to get 75 Kb
      Abstract: The reconstruction of
      the observer motion and environment structure from the optic flow is considered
      for the case where the camera mapping is unknown. This mapping has therefore
      to be estimated from a given set of examples, the training set. Since this
      mapping is not a function in the sense of a $m$ to $ map , standard neural
      networks are unable to learn this mapping. We propose to represent these
      mappings from $\calX\rightarrow\calY$ as a manifold in the product space
      $\calX\times\calY$. We approximate a parameterization of the manifold from
      a given set of data points by using an auto association network with a \em
      bottleneck layer. A gradient descend algorithm is used on the trained network
      to find the approximation of the egomotion and scene structure for a given
      set of optic flow vectors.

      PICTURE
      Dev, A. and Kröse,
      B.J.A. and Groen, F.C.A.
      (1995)

      “ Recovering Patch Parameters from The Optic
      Flow using Auto Associative Neural Networks,” Proceedings of the 1995
      International Conference on Intelligent Autonomous Systems,
      pp. 213-216

      Postscript file: click
      here to get 72 Kb
      Keywords: Structure from Motion,
      time-to-contact, neural networks, Multi valued mappings
      Abstract: The reconstruction of
      the observer motion and environment structure from optic flow is considered
      for the case where the camera mapping is unknown. This mapping has therefore
      to be estimated from a given set of examples, the training set. Since this
      mapping is not a function in the sense of a many to one mapping, standard
      neural networks are unable to learn this mapping. We propose to represent
      these mappings as a manifold in the product space. We approximate a parameterization
      of the manifold from a given set of data points by using an auto associative
      neural network with a bottleneck layer. A gradient descent algorithm is
      used on the parameterization of the learned manifold to find the approximation
      of the ego-motion for a given set of optic flow vectors.

      PICTURE
      Kröse, B.J.A.
      (1995)

      “ Learning from delayed rewards,” Robotics
      and Autonomous Systems
      15 , pp. 233-235

      Postscript file: click
      here to get 31 Kb
      Note: Editorial paper

      PICTURE
      Smagt,
      P.P. van der and Groen, F.C.A. and Kröse, B.J.A.
      (1995)

      “ A Monocular Robot Arm can be Neurally Positioned,”
      Proceedings of the 1995 International Conference on Intelligent
      Autonomous Systems,
      (Rembold, U. and Dillmann, R. and Hertzberger, L.O.
      and Kanade, T., ed.), IOS Press, pp. 123-130

      Postscript file: click
      here to get 101 Kb
      Keywords: time-to-contact, neural
      networks, hand-eye coordination, robot arm control, monocular vision
      Abstract: In this paper we introduce
      a method for model-free monocular visual guidance of a robot arm. The robot
      arm, with a single camera in its end-effector, should be positioned above
      a visually observed target. It is shown that a trajectory can be planned
      in visual space by using components of the optic flow, and this trajectory
      can be translated to joint torques by a self-learning neural network. No
      model of the robot, camera, or environment is used. The method reaches a
      high grasping accuracy after only a few trials.

      PICTURE
      Smagt, P.P. van der
      and Kröse, B.J.A.
      (1995)

      “ Using Many-Particle Decomposition to get
      a Parallel Self-Organising Map,” Proceedings of the 1995 Conference
      on Computer Science in the Netherlands,
      (Vliet, J. van , ed.), pp. 241-249

      Postscript file: click
      here to get 97 Kb
      Abstract: We propose a method for
      decreasing the computational complexity of self-organising maps. The method
      uses a partitioning of the neurons into disjoint clusters. Teaching of the
      neurons occurs on a cluster-basis instead of on a neuron-basis. For teaching
      an N-neuron network with N’ samples, the computational complexity decreases
      from O(NN’) to O(N log N’). Furthermore, we introduce a measure for the
      amount of order in a self-organising map, and show that the introduced algorithm
      behaves as well as the original algorithm.

      PICTURE
      Vy\vsniauskas,
      V. and Groen, F.C.A. and Kröse, B.J.A.
      (1995)

      “ Orthogonal incremental learning of a feedforward
      network,” Proceedings of the International Conference on Artificial
      Neural Networks, Paris,
      (Fogelman-Soulie and Gallinari, ed.), pp. 311-316

      Postscript file: click
      here to get 52 Kb
      Abstract: Orthogonal incremental
      learning (OIL) is a new approach of incremental training for a feedforward
      network with a single hidden layer. OIL is based on the idea to describe the
      output weights (but not the hidden nodes) as a set of orthogonal basis functions.
      Hidden nodes are treated just as the orthogonal representation of the network
      in the output weights domain. We showed that the network training can be
      performed incrementally, one node at time, and there is no need to use an
      additional constraint to support a consistent optimization among the hidden
      nodes. An advantage of OIL over existing algorithms is extremely fast learning.
      This approach can be also easily extended to build-up incrementally an arbitrary
      function as a linear composition of adjustable functions which are not necessarily
      orthogonal. We tested this approach on a standard “two-spirals” benchmark
      problem to build incrementally a feedforward network with a single layer
      of Gaussian units.

      PICTURE
      Dam, J.W.M.
      van and Kröse, B.J.A. and Groen, F.C.A.
      (May 1994)

      “ Optimising local Hebbian learning: use the
      $\delta$-rule,” Artificial Neural Networks, (Marinaro, M. and Morasso,
      P.G. , ed.), Springer-Verlag, pp. 631-634

      Postscript file: click
      here to get 47 Kb

      PICTURE
      Dam, J.W.M. van
      and Kröse, B.J.A. and Groen, F.C.A.
      (May 1994)

      “ CNN: a neural architecture that learns multiple
      transformations of spatial representations,” Artificial Neural Networks,
      (Marinaro, M. and Morasso, P.G., ed.), Springer-Verlag, pp. 1420-1423

      Postscript file: click
      here to get 42 Kb

      PICTURE
      Dam, J.W.M. van
      and Kröse, B.J.A. and Groen, F.C.A.
      (Oct. 1994)

      “ Transforming the ego-centered internal representation
      of an Autonomous robot with the Cascaded Neural Network,” Multisensor
      fusion and integration for intelligent systems,
      (Luo, R.C., ed.), IEEE,
      Piscataway, NJ, pp. 667-674

      Postscript file: click
      here to get 88 Kb

      PICTURE
      Dev, A. and
      Kröse, B.J.A. and Dorst, L. and Groen, F.C.A.
      (1994)

      “ Observer Curve and Object Detection from
      the Optic Flow,” Proceedings of the SPIE on Intelligent Robots and Computer
      Vision XIII,
      pp. 38-49

      Postscript file: click
      here to get 110 Kb
      Keywords: Structure from Motion,
      time-to-contact, Navigation, Mobile Robots, Curvature scaled depth
      Abstract: The robot is equipped
      with monocular vision to sense its environment. Motion of the robot results
      in motion of the environment in the sensory domain. The optic flow equals
      the projection of the environment motion on the image plane. We show that
      under a continuity assumption, the collision points can be computed from the
      optic flow without deriving a model of the environment. We will mainly consider
      a mobile robot. We derive the collision points by introducing an invariant,
      the curvature scaled depth. This invariant couples the rotational velocity
      of the robot to its translational velocity and is closely related to the
      curvature of the mobile robot’s path. We show that the spatial derivatives
      of the curvature scaled depth give the object surface orientation.

      PICTURE
      Dev, A. and
      Kröse, B.J.A., Dorst, L. and Groen, F.C.A.
      (Jul. 1994)

      “ Observer Curve and Obstacle Detection from
      Optic Flow,” TR. CS-94-11, Dept. of Comp. Sys, University of Amsterdam

      Postscript file: click
      here to get 140 Kb

      PICTURE
      Kröse,
      B.J.A. and Eecen, M.
      (1994)

      “ Self-learning maps for path planning in sensor
      space,” ICANN’94, Proceedings of the International Conference on Artificial
      Neural Networks,
      (Marinaro, M. and Morasso, P.G., ed.), Springer-Verlag,
      pp. 1303-1306


      PICTURE
      Kröse, B.J.A. and Eecen, M. (1994)

      “ A self-organizing representation of sensor
      space for mobile robot navigation,” Proceedings of the IEEE/RSJ/GI International
      Conference on Intelligent Robots and Systems,
      IEEE, pp. 9-14

      Postscript file: click
      here to get 136 Kb
      Keywords: mobile robot navigation,
      sensor-based planning, environment modelling, neural network techniques
      Abstract: The paper describes a
      sensor based navigation scheme which makes use of a global representation
      of the environment by means of a self-organizing map or Kohonen network. In
      contrast to existing methods for self-organizing environment representation,
      this discrete map is not represented in the world domain or in the configuration
      space of the vehicle, but in the sensor domain. The map is built by exploration.
      A conventional path planning technique now gives a path from current state
      to a desired state in the sensor domain, which can be followed using sensor
      based control. Collisions with obstacles are detected and used in the path
      planning. Results from a simulation show that the learned representation
      gives correct paths from an arbitrary starting point to an arbitrary end
      point.

      PICTURE
      Kröse, B.J.A.
      and Smagt, P.P. van der
      (1994)

      An Introduction to Neural Networks, University
      of Amsterdam, Amsterdam, The Netherlands

      Postscript file: click
      here to get 438 Kb
      Published as: lecture book

      PICTURE
      Schram, G. and
      Karsten, L. and Kröse, B.J.A. and Groen, F.C.A.
      (1994)

      “ Optimal Attitude Control of Satellites by
      Artificial Neural Networks: a Pilot Study,” Preprints of IFAC Symposium
      on Artificial Intelligence in Real-Time Control (AIRTC94),
      (Crespo, A.
      , ed.), Universidad Politechnica de Valencia, Servicio de Publicaciones,
      pp. 185-190

      Postscript file: click
      here to get 100 Kb
      Abstract: A pilot study is described
      on the practical application of artificial neural networks. The limit cycle
      of the attitude control of a satellite is selected as the test case. One of
      the sources of the limit cycle is a position dependent error in the observed
      attitude. A Reinforcement Learning method is selected, which is able to
      adapt a controller such that a cost function is optimised. An estimate of
      the cost function is learned by a neural critic. In our approach, the estimated
      cost function is directly represented as a function of the parameters of
      a linear controller. The critic is implemented as a CMAC network. Results
      from simulations show that the method is able to find optimal parameters
      without unstable behaviour. In particular in the case of large discontinuities
      in the attitude measurements, the method shows a clear improvement compared
      to the conventional approach: the RMS attitude error decreases approximately
      30 procent.

      PICTURE
      Schram, G. and
      Linden, F.X. van der and Kröse, B.J.A. and Groen, F.C.A.
      (Aug. 1994)

      “ Predictive Robot Control with Neural Networks,”
      TR. CS-94-13, Dept. of Comp. Sys, University of Amsterdam

      Postscript file: click
      here to get 96 Kb
      Abstract: Neural controllers are
      able to position the hand-held camera of the (3DOF) anthropomorphic OSCAR-robot
      manipulator above an object which is arbitrary placed on a table. The desired
      camera-joint mapping is approximated by feedforward neural networks. However,
      if the object is moving, the manipulator lags behind because of the required
      time to preprocess the visual information and to move the manipulator. Through
      the use of time derivatives of the position of the object and of the manipulator,
      the controller can inherently predict the next position of the object. In
      this paper several predictive controllers are proposed, and successfully
      applied to track a moving object.

      PICTURE
      Bartholomeus, M.G.P.
      and Kröse, B.J.A. and Noest, A.J.
      (Nov. 1993)

      “ A robust multi-resolution vision system for
      target tracking with a moving camera,” Computer Science in The Netherlands,
      (Wijshof, H. , ed.), CWI, Amsterdam, pp. 52-63

      Postscript file: click
      here to get 197 Kb

      PICTURE
      Dam, J.W.M. van
      and Kröse, B.J.A. and Groen, F.C.A.
      (Sep. 1993)

      “ Transforming Occupancy grids under robot
      motion,” Artificial neural networks, (Gielen, S. and Kappen,
      B., ed.), Springer-Verlag, pp. 318

      Postscript file: click
      here to get 52 Kb

      PICTURE
      Dam, J.W.M. van
      and Kröse, B.J.A. and Groen, F.C.A.
      (Nov. 1993)

      “ A neural network that transforms occupancy
      grids by parallel Monte-Carlo estimation,” Computing Science in The
      Netherlands,
      (Wijshoff, H.A. , ed.), CWI, Amsterdam, pp. 121-131

      Postscript file: click
      here to get 81 Kb

      PICTURE
      Groen, F.C.A. and
      Kröse, B.J.A. and Smagt, P.P. van der and Bartholomeus, M.G.P. and Noest,
      A.J.
      (Sep. 1993)

      “ Neural Networks for robot eye-hand coordination,”
      Artificial neural networks, (Gielen, S. and Kappen, B., ed.),
      Springer-Verlag, pp. 211-218


      PICTURE
      Kröse, B.J.A. and Compagner, K.
      and Groen, F.C.A.
      (1993)

      “ Accurate estimation of environment parameters
      from ultrasonic data,” Robotics and Autonomous Systems 11 (3/4),
      pp. 221-230


      PICTURE
      Kröse, B.J.A. and Smagt, P.P. van
      der and Groen, F.C.A.
      (1993)

      “ A one-eyed self-learning robot manipulator,”
      Neural networks in robotics, (Bekey, G. and Goldberg, K., ed.),
      Kluwer Academic Publishers, Dordrecht, pp. 19-28

      Postscript file:click
      here to get 77 Kb
      Keywords: neural networks, robot
      arm control, hand-eye coordination, monocular vision
      Abstract: A self-learning, adaptive
      control system for a robot arm using a vision system in a feedback loop is
      described. The task of the control system is to position the end-effector
      as accurate as possible directly above a target object, so that it can be
      grasped. The camera of the vision system is positioned in the end-effector
      and the visual information is used directly to control the robot. Two strategies
      are presented to solve the problem of obtaining 3D information from a single
      camera: a) using the size of the target object and b) using information
      from a sequence of images from the moving camera. In both cases a neural
      network is trained to perform the desired mapping.

      PICTURE
      Smagt, P.P. van
      der and Groen, F.C.A. and Kröse, B.J.A.
      (Oct. 1993)

      “ Robot hand-eye coordination using neural
      networks,” TR. CS-93-10, Dept. of Comp. Sys, University of Amsterdam

      Postscript file: click
      here to get 493 Kb
      Keywords: feed-forward neural networks,
      robot arm control, hand-eye coordination
      Abstract: This paper focuses on
      static hand-eye coordination. The key issue that will be addressed is the
      construction of a controller that eliminates the need for calibration. Instead,
      the system should be self-learning and must be able to adapt itself to changes
      in the environment. In this application, only positional information in
      the system will be used; hence the above reference `static.’ Three coordinate
      domains are used to describe the system: the Cartesian world-domain, the
      vision domain, and the robot domain. The task that is set out to be solved
      is the following. A robot manipulator has to be positioned directly above
      a pre-specified target, such that it can be grasped. The target is specified
      in terms of visual parameters. Only the (x,y,z) position of the end-effector
      relative to the target is taken into account; this suffices for many pick-and-place
      problems encountered in industry. (In a number of cases, also the rotation
      of the hand is of importance, but this rotation can be executed separate
      from the 3D positioning problem.) Thus the remaining problem is 3 degrees-of-freedom
      (DoF).

      PICTURE
      Vy\vsniauskas,
      V. and Groen, F.C.A. and Kröse, B.J.A.
      (Sep. 1993)

      “ A method for finding the optimal number of
      learning samples and hidden units for function approximation with a feed
      forward network,” Artificial neural networks, (Gielen, S. and Kappen,
      B., ed.), Springer-Verlag, pp. 550-553

      Keywords: Feedforward networks,
      function approximation, hidden units
      Abstract: This paper presents a
      methodology to estimate the optimal number of learning samples and the number
      of hidden units needed to obtain a desired accuracy of a function approximation
      by a feedforward network. The representation error and the generalization
      error, components of the total approximation error are analyzed and the
      approximation accuracy of a feedforward network is investigated as a function
      of the number of hidden units and the number of learning samples. Based on
      the asymptotical behaviour of the approximation error, an asymptotical model
      of the error function (AMEF) is introduced of which the parameters can be
      determined experimentally. In combination with knowledge about the computational
      complexity of the learning rule an optimal learning set size and number of
      hidden units can be found resulting in a minimum computation time for a given
      desired precision of the approximation.

      PICTURE
      Vy\vsniauskas,
      V. and Groen, F.C.A. and Kröse, B.J.A.
      (Nov. 1993)

      “ The optimal number of learning samples and
      hidden units in function approximation with a feedforward network,” TR.
      CS-93-15, Dept. of Comp. Sys, Univ. of Amsterdam

      Postscript file: click
      here to get 137 Kb
      Keywords: Feedforward networks,
      function approximation, continuous mapping, learning from examples, generalization,
      hidden units
      Abstract: This paper presents a
      method to estimate the optimal number of learning samples and the number of
      hidden units for a function approximation by a feedforward network. The optimality
      is considered under the minimal learning time constraint for a given degree
      of accuracy which is an essential point for real-time learning. The approximation
      error is modeled as a function of the number of hidden units and the number
      of learning samples. Two models are presented: the first one is based on
      general bounds of approximation and the second one on an asymptotic expansion
      of the approximation error. This approach was applied to optimize the learning
      of the camera-robot mapping of a visually guided robot arm and a complex
      logarithm function approximation. The results of this investigation suggested
      that the actual approximation errors differ considerably from the theoretical
      upper bounds.

      PICTURE
      Kröse,
      B.J.A. and Dam, J.W.M. van
      (Jun. 1992)

      “ Adaptive state space quantisation for reinforcement
      learning of collision-free navigation,” Proceedings of the 1992 IEEE/RSJ
      International Conference on Intelligent Robots and Systems ,
      IEEE, Piscataway,
      NJ, pp. 1327-1332

      Postscript file: click
      here to get 67 Kb

      PICTURE
      Kröse, B.J.A.
      and Dam, J.W.M. van
      (1992)

      “ Adaptive state space quantisation : Adding
      and removing neurons,” Artificial Neural Networks,2, (Aleksander,
      I. and Taylor, J. , ed.), North-Holland/Elsevier Science Publishers, Amsterdam,
      pp. 619-624

      Postscript file: click
      here to get 44 Kb

      PICTURE
      Kröse, B.J.A.
      and Dam, J.W.M. van
      (Jun. 1992)

      “ Learning to avoid collisions: a reinforcement
      learning paradigm for mobile robot navigation,” Proceedings of the 1992
      IFAC/IFIP/IMACS Symposium on Artificial Intelligence in Real-Time control,

      IFAC, pp. 295-301

      Postscript file: click
      here to get 47 Kb

      PICTURE
      Kröse, B.J.A.
      and Bartholomeus, M.G.P. and C.G. Gielen and Noest, A.J. and Smagt, P.P. van
      der
      (Apr. 1992)
      “ Visually controlled
      manipulator movements: the SNN demo project,” Proceedings of the 2nd
      Symposium on neural networks,
      Foundation for Neural Networks, Nijmegen,
      pp. 6-10


      PICTURE
      Smagt, P.P. van der and Kröse, B.J.A.
      and Groen, F.C.A.
      (Jun. 1992)

      “ A self-learning controller for monocular
      grasping,” Proceedings of the 1992 IEEE/RSJ International Conference
      on Intelligent Robots and Systems,
      IEEE, pp. 177-182

      Postscript file: click
      here to get 68 Kb
      Keywords: time-to-contact, neural
      networks, hand-eye coordination, robot arm control, monocular vision
      Abstract: A method is presented
      to learn 3D grasping of objects with unknown dimensions using a monocular
      eye-in-hand manipulator. From a sequence of images a motion profile is generated
      to approach the object of unknown size. It is shown that monocular visual
      information suffices to control the deceleration of the robot manipulator.
      A strategy for generating learning samples is presented, and simulation
      results demonstrate the effectiveness of the method.

      PICTURE
      Smagt, P.P. van
      der and Kröse, B.J.A. and Groen, F.C.A.
      (1992)

      “ A Cyclops Learns to Grasp,” Proceedings
      of the Second Symposium on Neural Networks,
      The Dutch Foundation for
      Neural Networks, pp. 88


      PICTURE
      Verschure, P.F.M.J. and Pfeifer, R. and
      Kröse, B.J.A.
      (1992)

      “ Distributed Adaptive Control: the self organization
      of structured behavior,” Robotics and Autonomous Systems 9 (2),
      pp. 181-196


      PICTURE
      Smagt, P.P. van der and Kröse, B.J.A.
      (June 1991)

      “ A Real-Time Learning Neural Robot Controller,”
      Proceedings of the 1991 International Conference on Artificial
      Neural Networks,
      (Kohonen, T. and Mäkisara, K. and Simula, O. and
      Kangas, J., ed.), North-Holland/Elsevier Science Publishers, pp. 351-356

      Postscript file: click
      here to get 48 Kb
      Keywords: neural networks, conjugate
      gradient learning, hand-eye coordination, robot arm control
      Abstract: A neurally based adaptive
      controller for a 6 degrees of freedom (DOF) robot manipulator with only rotary
      joints and a hand-held camera is described. The task of the system is to place
      the manipulator directly above an object that is observed by the camera (i.e.,
      2D hand-eye coordination). The requirement of adaptivity results in a system
      which does not make use of any inverse kinematics formulas or other detailed
      knowledge of the plant; instead, it should be self-supervising and adapt
      on-line. The proposed neural system will directly translate the preprocessed
      sensory data to joint displacements. It controls the plant in a feedback loop.
      The robot arm may make a sequence of moves before the target is reached, when
      in the meantime the network learns from experience. The network is shown to
      adapt quickly (in only tens of trials) and form a correct mapping from input
      to output domain.

      PICTURE
      Groen, F.C.A. and
      Kröse, B.J.A. and Smagt, P.P. van der
      (May 1991)

      “ Parallel Distributed Processing in Autonomous
      Robot Systems,” Proceedings of the 1991 Symposium on Neural Networks,
      The Dutch Foundation for Neural Networks, pp. 24-25

      PICTURE
      R.P.W. Duin and
      Kröse, B.J.A.
      (1980)

      “ On the possibility of avoiding peaking.”
      Proceedings 5th Int. Conf. on Pattern Recognition, 1980, Miami,
      U.S.A.


      Psychophysics

      PICTURE
      Kröse, B.J.A
      (1985)

      “ A Structure Description of Visual Information,”
      Pattern Recognition Letters, 3 (1985), 41-50.

      .

      PICTURE
      Kröse, B.J.A
      (1986)

      “ A Description of Visual Structure,” PhD.
      Thesis ,
      Delft 1986.

      PICTURE
      Kröse, B.J.A
      (1987)

      “Local structure analyzers as determinants
      of preattentive pattern discrimination,” Biological Cybernetics
      55, 286-298 (1987).

      PICTURE
      G.J.F. Smets, P.J. Stappers and
      Kröse, B.J.A (1988)

      “Form detection: features or invariance”.
      Perceptual and Motor Skills, 67, 311-317 (1988).

      PICTURE
      B. Julesz and
      Kröse, B.J.A (1988)

      “Visual texture perception: features and spatial
      filters.” Nature 333, 302-303 (1988).

      PICTURE
      Kröse, B.J.A
      and B. Julesz
      (1989)

      “The control and speed of shifts of attention”.
      Vision Research 29 (11) 1607-1619 (1989).

      .

      PICTURE
      Kröse, B.J.A
      and C.A. Burbeck
      (1989)

      “Spatial Interactions in rapid pattern discrimination.
      Spatial Vision 4 (4) 211-222 (1989)

       

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