{"id":153325,"date":"2007-01-01T00:00:00","date_gmt":"2007-01-01T00:00:00","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/msr-research-item\/a-switched-gaussian-process-for-estimating-disparity-and-segmentation-in-binocular-stereo\/"},"modified":"2018-10-16T20:10:34","modified_gmt":"2018-10-17T03:10:34","slug":"a-switched-gaussian-process-for-estimating-disparity-and-segmentation-in-binocular-stereo","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/a-switched-gaussian-process-for-estimating-disparity-and-segmentation-in-binocular-stereo\/","title":{"rendered":"A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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. 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