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Titlebook: Image Analysis and Recognition; 17th International C Aurélio Campilho,Fakhri Karray,Zhou Wang Conference proceedings 2020 Springer Nature S

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書目名稱Image Analysis and Recognition
副標(biāo)題17th International C
編輯Aurélio Campilho,Fakhri Karray,Zhou Wang
視頻videohttp://file.papertrans.cn/462/461393/461393.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Image Analysis and Recognition; 17th International C Aurélio Campilho,Fakhri Karray,Zhou Wang Conference proceedings 2020 Springer Nature S
描述.This two-volume set LNCS 12131 and LNCS 12132 constitutes the refereed proceedings of the 17th International Conference on Image Analysis and Recognition, ICIAR 2020, held in Póvoa de Varzim, Portugal, in June 2020..The 54 full papers presented together with 15 short papers were carefully reviewed and selected from 123 submissions. The papers are organized in the following topical sections: image processing and analysis; video analysis; computer vision; 3D computer vision; machine learning; medical image and analysis; analysis of histopathology images; diagnosis and screening of ophthalmic diseases; and grand challenge on automatic lung cancer patient management..Due to the corona pandemic, ICIAR 2020 was held virtually only..
出版日期Conference proceedings 2020
關(guān)鍵詞image and video coding; image retrieval and indexing; video processing and analysis; machine learning; o
版次1
doihttps://doi.org/10.1007/978-3-030-50516-5
isbn_softcover978-3-030-50515-8
isbn_ebook978-3-030-50516-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

書目名稱Image Analysis and Recognition影響因子(影響力)




書目名稱Image Analysis and Recognition影響因子(影響力)學(xué)科排名




書目名稱Image Analysis and Recognition網(wǎng)絡(luò)公開度




書目名稱Image Analysis and Recognition網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Image Analysis and Recognition被引頻次




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Parallel Implementation of the DRLSE Algorithma purely serial C++ implementation we developed, and gains between roughly 180% and 500%, when comparing against the MATLAB reference implementation of DRLSE, both depending on input image resolution.
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Object Tracking Through Residual and Dense LSTMsseLSTMs outperform Residual and regular LSTM, and offer a higher resilience to nuisances such as occlusions and out-of-view objects. Our case study supports the adoption of residual-based RNNs for enhancing the robustness of other trackers.
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