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Titlebook: Computer Vision – ACCV 2018; 14th Asian Conferenc C. V. Jawahar,Hongdong Li,Konrad Schindler Conference proceedings 2019 Springer Nature Sw

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發(fā)表于 2025-3-21 17:18:38 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computer Vision – ACCV 2018
副標(biāo)題14th Asian Conferenc
編輯C. V. Jawahar,Hongdong Li,Konrad Schindler
視頻videohttp://file.papertrans.cn/235/234122/234122.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision – ACCV 2018; 14th Asian Conferenc C. V. Jawahar,Hongdong Li,Konrad Schindler Conference proceedings 2019 Springer Nature Sw
描述The six volume set LNCS 11361-11366 constitutes the proceedings of the 14.th. Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.
出版日期Conference proceedings 2019
關(guān)鍵詞artificial intelligence; classification; computer vision; data security; estimation; face recognition; fea
版次1
doihttps://doi.org/10.1007/978-3-030-20890-5
isbn_softcover978-3-030-20889-9
isbn_ebook978-3-030-20890-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

書目名稱Computer Vision – ACCV 2018影響因子(影響力)




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Cross-Spectral Image Patch Matching by Learning Features of the Spatially Connected Patches in a Shan. Extensive experiments shows that SCFDM outperforms the state-of-the-art methods on the cross-spectral dataset in terms of FPR95 and the training convergence. Meanwhile, it also demonstrates a better generalizability on a single spectral dataset.
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Robust Video Background Identification by Dominant Rigid Motion Estimationotions are also taken care of by checking their global consistency with the final estimated background motion. Lastly, by virtue of its efficiency, our method can deal with densely sampled trajectories. It outperforms several state-of-the-art motion segmentation methods on public datasets, both quan
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SMC: Single-Stage Multi-location Convolutional Network for Temporal Action Detectionation offsets to the default locations, as well as action categories. SMC in practice is faster than the existing methods (753 FPS on a Titan X Maxwell GPU) and achieves state-of-the-art performance on THUMOS’14 and MEXaction2.
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Neural Abstract Style Transfer for Chinese Traditional Paintinginting. To promote research on this direction, we collect a new dataset with diverse photo-realistic images and Chinese traditional paintings (The dataset will be released at ..). In experiments, the proposed method shows more appealing stylized results in transferring the style of Chinese tradition
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