Computer Models for Biological Vision Systems
Computer Models for Biological Vision Systems
Disciplines
Computer Sciences (50%); Medical-Theoretical Sciences, Pharmacy (50%)
Each of us might have encountered the situation to desperately search for a personal item or a location in an unknown environment. At present there is no technical solution for such an assistive system. The newly granted Joint Research Project "Cognitive Vision" attempts to find first solutions in this direction. A human shall be supported with a system that can not only find things, but that can understand the relationship between the human activities and objects involved. This understanding of new information and new knowledge is the key aspect of the cognitive approach to computer vision. The solution proposed is based on a trans-disciplinary approach. It integrates partners from theoretical computer science (TU Graz), neuroscience (Max-Planck-Institut Tübingen), artificial intelligence (ÖFAI, Wien), machine learning (MU Leoben), user engineering (CURE, Wien) and different areas of computer vision and pattern recognition (ACIN & PRIP TU Wien, EMT & ICG TU Graz and Joanneum Research Graz). One aspect of the project is to investigate the relations of the different brain regions in visual cortex. While individual functions of these regions are relatively well studied, new methods of screening brain functions enable deeper insights that contradict present hypotheses. It could be shown that human vision profits enormously from expectations in a given situation. For example, objects in an atypical environment are spotted much more quickly than in the expected environment. Using this analysis of the only "working" vision system we will develop computer models to describe objects under different conditions, for example, different illumination, shape, scale, clutter and occlusion, and to describe the relationships between objects and the environment. A particular emphasis is on learning these models and relationships. In the same way one shows a new object to a child, we want to relieve the user from the present exhaustive learning phases. Another aspect of the research work is the analysis of the interrelations of the different seeing functions, namely, mechanisms to guide attention, the detection and identification of objects, the prediction of motions and intentions of the user, the integration of knowledge of the present situation, and the creation of an appropriate system reaction. The coordination of these functions is resolved using an agent/based optimisation of the utility to the system`s functioning. The techniques devised will be implemented in prototype systems. In a user study it will be evaluated how the expectations are met or not to further improve system performance. The objective of the next three years is to track and predict where objects are moved to and where locations can be found. A user could then ask the system where her mug is or where a specific shop is when entering unknown parts of a city. In both cases the user would be assisted and guided to the location.
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consortium member (15.12.2003 - 14.12.2009)
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consortium member (15.12.2003 - 31.12.2006)
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consortium member (15.12.2003 - 14.11.2006)
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consortium member (15.12.2003 - 31.12.2006)
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consortium member (15.12.2003 - 15.12.2009)
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consortium member (07.10.2003 - 06.11.2006)
- Max-Planck-Gesellschaft
- Technische Universität Graz
- Lucas Paletta, Joanneum Research , associated research partner
- Horst Bischof, Technische Universität Graz , associated research partner
- Walter G. Kropatsch, Technische Universität Wien , associated research partner
Research Output
- 229 Citations
- 7 Publications
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2011
Title Hierarchical spatio-temporal extraction of models for moving rigid parts DOI 10.1016/j.patrec.2011.05.005 Type Journal Article Author Artner N Journal Pattern Recognition Letters Pages 2239-2249 Link Publication -
2009
Title Motif distribution, dynamical properties, and computational performance of two data-based cortical microcircuit templates DOI 10.1016/j.jphysparis.2009.05.006 Type Journal Article Author Haeusler S Journal Journal of Physiology-Paris Pages 73-87 -
2009
Title Spiking Neurons Can Learn to Solve Information Bottleneck Problems and Extract Independent Components DOI 10.1162/neco.2008.01-07-432 Type Journal Article Author Klampfl S Journal Neural computation Pages 911-959 -
2009
Title PCSIM: a parallel simulation environment for neural circuits fully integrated with Python DOI 10.3389/neuro.11.011.2009 Type Journal Article Author Pecevski D Journal Frontiers in Neuroinformatics Pages 11 Link Publication -
2010
Title Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1 DOI 10.1152/jn.00845.2009 Type Journal Article Author Rasch M Journal Journal of Neurophysiology Pages 757-778 Link Publication -
2010
Title Reinforcement Learning on Slow Features of High-Dimensional Input Streams DOI 10.1371/journal.pcbi.1000894 Type Journal Article Author Legenstein R Journal PLoS Computational Biology Link Publication -
2010
Title A Theoretical Basis for Emergent Pattern Discrimination in Neural Systems Through Slow Feature Extraction DOI 10.1162/neco_a_00050 Type Journal Article Author Klampfl S Journal Neural computation Pages 2979-3035