Authors:
Junlei Ma
;
Dianle Zhou
;
Chen Chen
and
Wei Wang
Affiliation:
National University of Defense Technology, China
Keyword(s):
Stereo matching, Automatic matting, Iterative optimization.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image Understanding
;
Image-Based Modeling
;
Object Recognition
;
Pattern Recognition
;
Software Engineering
Abstract:
This study presents a novel iterative algorithm of joint depth and alpha matte optimization via stereo
(JDMOS). This algorithm realizes simultaneous estimation of depth map and matting image to obtain final
convergence. The depth map provides depth information to realize automatic image matting, whereas the
border details generated from the image matting can refine the depth map in boundary areas. Compared with
monocular matting methods, another advantage offered by JDMOS is that the image matting process is
completely automatic, and the result is significantly more robust when depth information is introduced. The
major contribution of JDMOS is adding image matting information to the cost function, thereby refining the
depth map, especially in the scene boundary. Similarly, optimized disparity information is stitched into the
matting algorithm as prior knowledge to make the foreground–background segmentation more accurate.
Experimental results on Middlebury datasets demonstr
ate the effectiveness of JDMOS.
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