Abstract
Stereoscopic image sequence processing has been the focus of considerable attention in recent literature for videoconference applications. A novel Bayesian scheme is proposed in this paper, for the segmentation of a noisy stereoscopic image sequence. More specifically, occlusions and visible foreground and background regions are detected between the left and the right frame while the uncovered-background areas are identified between two successive frames of the sequence. Combined hypotheses are used for the formulation of the Bayes decision rule which employs a single intensity-difference measurement at each pixel. Experimental results illustrating the performance of the proposed technique are presented and evaluated in videoconference applications.
Highlights
Stereo vision provides a direct way of inferring the depth information and 3D perception by using two images destined for the left and right eye, respectively
We introduce a novel Bayesian scheme for the segmentation of a videoconference noisy stereoscopic image sequence
A Bayes decision test is employed for detecting occlusions, visible foreground, and background regions and uncovered-background areas
Summary
Stereoscopic image sequence processing has been the focus of considerable attention in recent literature for videoconference applications. A novel Bayesian scheme is proposed in this paper, for the segmentation of a noisy stereoscopic image sequence. Occlusions and visible foreground and background regions are detected between the left and the right frame while the uncovered-background areas are identified between two successive frames of the sequence. Combined hypotheses are used for the formulation of the Bayes decision rule which employs a single intensity-difference measurement at each pixel. Experimental results illustrating the performance of the proposed technique are presented and evaluated in videoconference applications. Keywords and phrases: Bayesian decision test, segmentation, stereoscopic video, disparity, motion
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