Titlebook: Computer Vision – ECCV 2016; 14th European Confer Bastian Leibe,Jiri Matas,Max Welling Conference proceedings 2016 Springer International P

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书目名称Computer Vision – ECCV 2016影响因子(影响力)




书目名称Computer Vision – ECCV 2016影响因子(影响力)学科排名




书目名称Computer Vision – ECCV 2016网络公开度




书目名称Computer Vision – ECCV 2016网络公开度学科排名




书目名称Computer Vision – ECCV 2016被引频次




书目名称Computer Vision – ECCV 2016被引频次学科排名




书目名称Computer Vision – ECCV 2016年度引用




书目名称Computer Vision – ECCV 2016年度引用学科排名




书目名称Computer Vision – ECCV 2016读者反馈




书目名称Computer Vision – ECCV 2016读者反馈学科排名

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Moral Education from the Dunhuang MuralsD image to 3D space. Aimed at improving the accuracy of 3D motion reconstruction, we introduce the additional built-in knowledge, namely height-map, into the algorithmic scheme of reconstructing the 3D pose/motion under a single-view calibrated camera. Our novel proposed framework consists of two ma

BUMP

Su Xiaojia (苏晓佳),Zhou Hongtao (周洪涛)on. We first define RBF kernels on 3D joint sequences, which are then linearized to form kernel descriptors. The higher-order outer-products of these kernel descriptors form our tensor representations. We present two different kernels for action recognition, namely (i) a . that captures the spatio-t

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MEEK

Product Durability and Re-Take after Usetterns, or curvilinear structures. In the general setting – without controlled acquisition, abundant texture, curves and surfaces following specific models or limiting scene complexity – most methods produce unorganized point clouds, meshes, or voxel representations, with some exceptions producing u

玉米

Management of Atypical Femoral Fractures,s a body shape with shape parameters, we describe a novel approach to automatically estimate these parameters from a single input shape silhouette using semi-supervised learning. By utilizing silhouette features that encode local and global properties robust to noise, pose and view changes, and proj

玉米

https://doi.org/10.1007/978-94-017-5962-5ition. Most existing SfT methods require well-textured surfaces that deform smoothly, which is a significant limitation. Due to the sparsity of correspondence constraint and strong regularizations, they usually fail to reconstruct strong changes of surface curvature such as surface creases. We inves

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Restoration And Indecision (1816 : 1829), key challenge is that the per-frame alignments between the input (video) and label (action) sequences are unknown during training. We address this by introducing the Extended Connectionist Temporal Classification (ECTC) framework to efficiently evaluate all possible alignments via dynamic programmi

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