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Titlebook: Computer Vision –ACCV 2016; 13th Asian Conferenc Shang-Hong Lai,Vincent Lepetit,Yoichi Sato Conference proceedings 2017 Springer Internatio

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楼主: 闸门
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Realtime Hierarchical Clustering Based on Boundary and Surface Statistics a distance metric. The core of this approach is our novel cluster distance: it combines boundary and surface statistics both in terms of appearance as well as spatial linkage. This yields state-of-the-art performance, as we demonstrate in conclusive experiments conducted on BSDS500 and Pascal-Context datasets.
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Weakly-Supervised Video Scene Co-parsingls and assigned to a semantic label with the maximum one. The proposed co-parsing framework extends scene parsing from single images to videos and exploits mutual information among a video collection. Experimental results on the Wild-8 and SUNY-24 datasets show that the proposed algorithm performs favorably against the state-of-the-art approaches.
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Object Boundary Guided Semantic Segmentationd class features elegantly in a fully convolutional way with a designed masking architecture. We conduct experiments on the PASCAL VOC segmentation benchmark, and show that the end-to-end trainable OBG-FCN system offers great improvement in optimizing the target semantic segmentation quality.
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Point-Cut: Interactive Image Segmentation Using Point Supervisionnto a graph cut optimization to generate binary segments. Intensive experiments show that our approach outperforms existing methods for interactive object segmentation both qualitatively and quantitatively.
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Realtime Hierarchical Clustering Based on Boundary and Surface Statisticsr high level tasks such as recognition. In this paper, we introduce an efficient, realtime capable algorithm which likewise agglomerates a valuable hierarchical clustering of a scene, while using purely local appearance statistics..To speed up the processing, first we subdivide the image into meanin
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