Titlebook: Computer Vision – ACCV 2018 Workshops; 14th Asian Conferenc Gustavo Carneiro,Shaodi You Conference proceedings 2019 Springer Nature Switzer

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书目名称Computer Vision – ACCV 2018 Workshops影响因子(影响力)




书目名称Computer Vision – ACCV 2018 Workshops影响因子(影响力)学科排名




书目名称Computer Vision – ACCV 2018 Workshops网络公开度




书目名称Computer Vision – ACCV 2018 Workshops网络公开度学科排名




书目名称Computer Vision – ACCV 2018 Workshops被引频次




书目名称Computer Vision – ACCV 2018 Workshops被引频次学科排名




书目名称Computer Vision – ACCV 2018 Workshops年度引用




书目名称Computer Vision – ACCV 2018 Workshops年度引用学科排名




书目名称Computer Vision – ACCV 2018 Workshops读者反馈




书目名称Computer Vision – ACCV 2018 Workshops读者反馈学科排名

Tdd526

Tatiana V. Nikulina,J. Patrick Kociolekdifficult to estimate the gaze of each person in a crowd accurately and simultaneously with existing image-based eye tracking methods, since the image resolution of each person becomes low when we capture the whole crowd with a distant camera. Therefore, we introduce a new approach for localizing th

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束缚

Spartan

充满人

https://doi.org/10.1007/978-1-4684-0409-8eatures for recognition are appeared in the partial regions of human, thus we segment a video frame into spatial regions based on the human body parts to enhance feature representation. We utilize an object detector and a pose estimator to segment four regions, namely full body, left/right arm, and

充满人

套索

https://doi.org/10.1007/978-1-349-03555-7sformation. Above all, a method called Style Transfer is drawing much attention which can integrate two photos into one integrated photo regarding their content and style. Although many extended works including Fast Style Transfer have been proposed so far, all the extended methods including origina

fatuity

hazard

https://doi.org/10.1007/978-1-349-03555-7techniques for visual recognition have encouraged new possibilities for computing aesthetics and other related concepts in images. In this paper, we design an approach for recognizing styles in photographs by introducing adapted deep convolutional neural networks that are attentive towards strong ne