A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo

This paper describes a Gaussian process framework for inferring pixel-wise disparity and bi-layer segmentation of a scene given a stereo pair of images. The Gaussian process covariance is parameterized by a foreground-background occlusion segmentation label to model both smooth regions and discontinuities. As such, we call our model a switched Gaussian process. We propose a greedy incremental algorithm for adding observations from the data and assigning segmentation labels. Two observation schedules are proposed: the first treats scanlines as independent, the second uses an active learning criterion to select a sparse subset of points to measure. We show that this probabilistic framework has comparable performance to the state-of-the-art.

NIPS2006_0468[1].pdf
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In  Advances in Neural Information Processing Systems 19

Publisher  MIT Press
All copyrights reserved by MIT Press 2007.

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TypeInproceedings
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