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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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Object Boundary Guided Semantic SegmentationN) has enabled accurate pixel-level labeling. One issue in previous works is that the FCN based method does not exploit the object boundary information to delineate segmentation details since the object boundary label is ignored in the network training. To tackle this problem, we introduce a double
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FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architectureadditional depth measurement will improve the accuracy. Here we investigate a solution how to incorporate complementary depth information into a semantic segmentation framework by making use of convolutional neural networks (CNNs). Recently encoder-decoder type fully convolutional CNN architectures
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A Holistic Approach for Data-Driven Object Cutouth typically contain considerable background clutter. In contrast to existing cutout methods, which are based mainly on low-level image analysis, we propose a more . approach, which considers the entire shape of the object of interest by leveraging higher-level image analysis and learnt global shape
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Interactive Segmentation from 1-Bit Feedbackion is to propose a sequence of yes-or-no questions to the user. Then, according to the 1-bit answers from the user, the segmentation algorithm progressively revises the questions and the segments, so that the segmentation result can approach the ideal region of interest (ROI) in the mind of the use
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234116.jpg
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978-3-319-54180-8Springer International Publishing AG 2017
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