Gates for handling occlusion in Bayesian models of images: an initial study

Authors:
Daniel Oberhoff;Dominik Endres;Martin A. Giese;Marina Kolesnik
Affiliations:
Fraunhofer FIT-LIFE, St. Augustin, Germany;Dept. of Cognitive Neurology, University Clinic, CIN, HIH and University of Tübingen, Tübingen, Germany;Dept. of Cognitive Neurology, University Clinic, CIN, HIH and University of Tübingen, Tübingen, Germany;Fraunhofer FIT-LIFE, St. Augustin, Germany
Venue:
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Year:
2011

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Abstract

Probabilistic systems for image analysis have enjoyed increasing popularity within the last few decades, yet principled approaches to incorporating occlusion as a feature into such systems are still few [11,10,7]. We present an approach which is strongly influenced by the work on noisyor generative factor models (see e.g. [3]). We show how the intractability of the hidden variable posterior of noisy-or models can be (conditionally) lifted by introducing gates on the input combined with a sparsifying prior, allowing for the application of standard inference procedures.We demonstrate the feasibility of our approach on a computer vision toy problem.