Date: Monday, February 25th 2013
Prof. Justus Piater
Intelligent and Interactive Systems
Institute of Computer Science, University of Innsbruck, Austria
Room: 5161.0267 (Bernoulliborg)
Hierarchical Models and Visual Inference in Humans and Machines
While the computational details of human vision remain largely obscure,it is clear that at least some important aspects of vision proceed bysuccessive abstraction over multiple levels from retinotopic featurerepresentations to high-level concepts. While this general idea has agreat deal of appeal to computational scientists, it is far from clearhow to turn it into practical systems. One may argue that this lack ofunderstanding slows progress in both computer vision and high-levelvisual neuroscience. In this talk I will review key aspects ofbiological vision that are relatively well understood, and will describeour own attempt at building a hierarchical vision system. At eachlevel, it constructs spatial distributions of juxtapositions of featuresor parts. Each level presents to the level above an abstraction of thelevel below, while presenting to the level below its own idea of whatshould be seen in the scene given the level above's sceneinterpretation. Together, these spatial distributions form a layeredMarkov network that models visual recognition as a bidirectionalprobabilistic inference procedure that seeks consensus between storedmodels and perceived stimuli at all levels of the hierarchy.
Colloquium coordinators are Prof.dr. M. Aiello (e-mail : M.Aiello rug.nl) and
Prof.dr. M. Biehl (e-mail: M.Biehl rug.nl)
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