The CogVis Project
The CogVis project (Cognitive Vision) was a European Union funded collaborative project which ran from 2001 to 2004 (Contract IST-2000-29375) to study the design of Cognitive Vision Systems. In this context a "cognitive vision system" is defined as a system that uses visual information to achieve:
|Protocol learning||Combining continuous and symbolic models to learn games from observation|
|Temporal Continuity||Enforcing global spatio-temporal consistency to enhance reliability of moving object tracking and classification|
|Traffic interaction||Modelling traffic interaction using learnt qualitative spatio-temporal relations and variable length Markov models|
|Car/blob tracking||A generic object tracker, demonstrated tracking vehicles|
The consortium homepage may be found here. Institutions making up the consortium are:
Various pieces of software developed under this project are available for download.
Needham, Chris J; Santos, Paulo E; Magee, Derek R; Devin, Vincent; Hogg, David C; Cohn, Anthony G. Protocols from perceptual observations. Artificial Intelligence, vol. 167, pp. 103-136. 2005.(PDF)
Magee, D R; Needham, C J; Santos, P E; Rao, S. Inducing the focus of attention by observing patterns in space in: IJCAI Workshop on Modelling Others from Observations (MOO 2005), pp. 47-52. 2005. (PDF)
Bennett, B; Magee, D; Cohn, A G; Hogg, D C. Using spatio-temporal continuity constraints to enhance visual tracking of moving objects in: Lopez de Mantaras, R & Saitta, L (editors) ECAI 2004 Proceedings of the 16th European Conference on Artificial Intelligence, pp. 922-926 IOS Press. 2004. (PDF)
Magee, D. Tracking multiple vehicles using foreground, background and motion models. Image and Vision Computing, vol. 22, pp. 143-155. 2004.
Magee, D R; Needham, C J; Santos, P; Cohn, A G; Hogg, D C. Autonomous learning for a cognitive agent using continuous models and inductive logic programming from audio-visual input in: Proceedings AAAI-04 Workshop on Anchoring Symbols to Sensor Data, pp. 17-24. 2004. (PDF)
Santos, P; Magee, D; Cohn, A G. Looking for logic in vision in: Proceedings Eleventh Workshop on Automated Reasoning, pp. 61-62. 2004.
Santos, Paulo; Magee, Derek; Cohn, Anthony; Hogg, David. Combining multiple answers for learning mathematical structures from visual observation in: Lopez de Mantaras, R & Saitta, L (editors) ECAI 2004 Proceedings of the 16th European Conference on Artificial Intelligence, pp. 544-548 IOS Press. 2004. (PDF)
Cohn, A G; Magee, D; Galata, A; Hogg, David; Hazarika, S. Towards an architecture for cognitive vision using qualitative spatio-temporal representations and abduction in: Freksa, C, Brauer, W, Habel, C & Wender, K F (editors) Spatial Cognition III, Routes and Navigation, Human Memory and Learning, Spatial Representation and Spatial Learning, pp. 232-248 Springer-Verlag. 2003. (PDF)
Magee, D. A sequential scheduling approach to combining multiple object classifiers using cross-entropy in: Windeatt, T & Roli, F (editors) Multiple Classifier Systems, pp. 135-145 Springer-Verlag. 2003. (PDF)
Magee, D; Liebe, B. Online face tracking using a feature driven level-set in: Harvey, R, & Bangham, A (editors) Proceedings of the 14th British Machine Vision Conference , pp. 419-428 BMVA. 2003.
Magee, D. Tracking multiple vehicles using foreground, background and motion models in: Proceedings ECCV Workshop on Statistical Methods in Video Processing, pp. 7-12. 2002. Available as a Research Report.
Magee, D. A qualitative, multi-scale grammar for image description and analysis in: British Machine Vision Conference 2002, pp. 293-302. 2002.
Galata, Aphrodite; Cohn, Anthony G; Magee, Derek; Hogg, David. Learning temporal and qualitative spatial components of an interaction model in: Proceedings ECCV Workshop on Vision and Modelling of Dynamic Scenes (VAMODS). 2002.
Galata, Aphrodite; Cohn, Anthony G; Magee, Derek; Hogg, David. Modeling interaction using learnt qualitative spatio-temporal relations and variable length Markov models in: van Harmelen, F (editors) Proceedings of the 15th European Conference on Artificial Intelligence (ECAI'02), pp. 741-745. 2002. (PDF)