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Computer Models for Biological Vision Systems

Computer Models for Biological Vision Systems

Markus Vincze (ORCID: 0000-0002-2799-491X)
  • Grant DOI 10.55776/S91
  • Funding program National Research Networks
  • Status ended
  • Start October 7, 2003
  • End December 15, 2009
  • Funding amount € 3,128,414
  • Project website

Disciplines

Computer Sciences (50%); Medical-Theoretical Sciences, Pharmacy (50%)

Abstract

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.

Consortium
  • Axel Pinz, Technische Universität Graz
    consortium member (15.12.2003 - 14.12.2009)
  • Manfred Tscheligi, Universität Salzburg
    consortium member (15.12.2003 - 31.12.2006)
  • Markus Vincze, Technische Universität Wien
    consortium member (15.12.2003 - 14.11.2006)
  • Paolo Petta, ÖFAI - Österreichisches Forschungsinstitut für Artifical Intelligence
    consortium member (15.12.2003 - 31.12.2006)
  • Peter Auer, Montanuniversität Leoben
    consortium member (15.12.2003 - 15.12.2009)
  • Wolfgang Maass, Technische Universität Graz
    consortium member (07.10.2003 - 06.11.2006)
Research institution(s)
  • Max-Planck-Gesellschaft
  • Technische Universität Graz
Project participants
  • 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
Publications
  • 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

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