Video Segmentation by MAP Labeling of Watershed Segments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Bi-Layer Segmentation of Binocular Stereo Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Video segmentation based on motion coherence of particles in a video sequence
IEEE Transactions on Image Processing
Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Bilayer Segmentation and Motion/Depth Estimation with a Handheld Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic segmentation of moving objects for video object plane generation
IEEE Transactions on Circuits and Systems for Video Technology
Efficient moving object segmentation algorithm using background registration technique
IEEE Transactions on Circuits and Systems for Video Technology
Automatic segmentation of moving objects in video sequences: a region labeling approach
IEEE Transactions on Circuits and Systems for Video Technology
Occlusion-Aware Motion Layer Extraction Under Large Interframe Motions
IEEE Transactions on Image Processing
Segmentation and Tracking Multiple Objects Under Occlusion From Multiview Video
IEEE Transactions on Image Processing
RGB-(D) scene labeling: Features and algorithms
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A novel layered stereoscopic moving-object segmentation method is proposed in this paper by exploiting both motion information and depth information to extract moving objects for each depth layer with high accuracy on their shape boundary. By taking a higher-order statistics on two frame-difference fields across three adjacent frames, the computed motion information are used to conduct change detection and generate one motion mask that consists of all the moving objects from all the depth layers involved at each view. It would be highly desirable, and challenging, to further differentiate them according to their residing depth layer to achieve layered segmentation. For that, multiple depth-layer masks are generated using our proposed disparity estimation method, one for each depth layer. By intersecting the motion mask and one depth-layer mask at any given layer-of-interest, the moving objects associated with the corresponding layer are then extracted. All the above-mentioned processes are repeatedly performed along the video sequence with a sliding window of three frames at a time. For demonstration, only the foreground and the background layers are considered in this paper, while the proposed method is generic and can be straightforwardly extended to more layers, once the corresponding depth-layer masks are made available. Experimental results have shown that the proposed layered moving-object segmentation method is able to segment the foreground and background moving objects separately, with high accuracy on their shape boundary. In addition, the required computational load is considered fairly inexpensive, since our design methodology is to generate masks and perform intersections for extracting the moving objects for each depth layer.