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Published in:
2011 | OriginalPaper | Chapter
Gates for Handling Occlusion in Bayesian Models of Images: An Initial Study
Authors : Daniel Oberhoff, Dominik Endres, Martin A. Giese, Marina Kolesnik
Published in: KI 2011: Advances in Artificial Intelligence
Publisher: Springer Berlin Heidelberg
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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
noisy-or
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.