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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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21#
發(fā)表于 2025-3-25 06:52:27 | 只看該作者
Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks)?we introduce a multi-kernel convolutional layer for fast aggregation of predictions at multiple scales; (3)?we perform data fusion from heterogeneous sensors (optical and laser) using residual correction. Our framework improves state-of-the-art accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset.
22#
發(fā)表于 2025-3-25 11:01:47 | 只看該作者
Conference proceedings 2017Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision..
23#
發(fā)表于 2025-3-25 14:31:44 | 只看該作者
0302-9743 Actions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision..978-3-319-54180-8978-3-319-54181-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
24#
發(fā)表于 2025-3-25 16:43:41 | 只看該作者
25#
發(fā)表于 2025-3-25 20:37:23 | 只看該作者
26#
發(fā)表于 2025-3-26 03:46:35 | 只看該作者
https://doi.org/10.1007/978-3-642-66090-0ructures in data without specifying the number of structures, but also handle data even with a large number of outliers. Experimental results on both synthetic data and real images further demonstrate the superiority of the proposed method over several state-of-the-art fitting methods.
27#
發(fā)表于 2025-3-26 07:34:37 | 只看該作者
28#
發(fā)表于 2025-3-26 12:12:09 | 只看該作者
29#
發(fā)表于 2025-3-26 14:23:41 | 只看該作者
https://doi.org/10.1007/978-3-642-68514-9d 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.
30#
發(fā)表于 2025-3-26 17:41:41 | 只看該作者
https://doi.org/10.1007/978-3-642-68719-8nto 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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