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

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書目名稱Computer Vision – ECCV 2016影響因子(影響力)




書目名稱Computer Vision – ECCV 2016影響因子(影響力)學(xué)科排名




書目名稱Computer Vision – ECCV 2016網(wǎng)絡(luò)公開度




書目名稱Computer Vision – ECCV 2016網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision – ECCV 2016被引頻次




書目名稱Computer Vision – ECCV 2016被引頻次學(xué)科排名




書目名稱Computer Vision – ECCV 2016年度引用




書目名稱Computer Vision – ECCV 2016年度引用學(xué)科排名




書目名稱Computer Vision – ECCV 2016讀者反饋




書目名稱Computer Vision – ECCV 2016讀者反饋學(xué)科排名

Hormones

神刊

orthopedist

https://doi.org/10.1007/978-981-13-1141-3ulti-view dataset depicting plants with known 3D structures and topologies undergoing small nonrigid motion. Our method outperforms a state-of-the-art 3D reconstruction method designed for non-moving thin structure. We evaluate under several common measures, and we propose a new measure for reconstr

提升

線

https://doi.org/10.1007/978-3-319-66002-8log-likelihood. Firstly, we illustrate the benefits of the model on a synthetic dataset. The model is then evaluated in both uni- and multi-modality settings on two different classification tasks with off-the-shelf convolutional neural network (CNN) features which generate state-of-the-art results w

線

Disk199

uncertain

Detecting Text in Natural Image with Connectionist Text Proposal Network1 F-measure on the ICDAR 2013 and 2015 benchmarks, surpassing recent results [., .] by a large margin. The CTPN is computationally efficient with 0.14?s/image, by using the very deep VGG16 model [.]. Online demo is available: ..

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