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2011
Conference Paper
Titel
Gates for handling occlusion in Bayesian models of images
Titel Supplements
An initial study
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 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.