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Titlebook: Computer Vision, Imaging and Computer Graphics Theory and Applications; 15th International J Kadi Bouatouch,A. Augusto de Sousa,Jose Braz C

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書目名稱Computer Vision, Imaging and Computer Graphics Theory and Applications
副標(biāo)題15th International J
編輯Kadi Bouatouch,A. Augusto de Sousa,Jose Braz
視頻videohttp://file.papertrans.cn/235/234311/234311.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Computer Vision, Imaging and Computer Graphics Theory and Applications; 15th International J Kadi Bouatouch,A. Augusto de Sousa,Jose Braz C
描述This book constitutes thoroughly revised and selected papers from the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020, held in Valletta, Malta, in February 2020..The 25 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 455 submissions. The papers contribute to the understanding of relevant trends of current research on computer graphics; human computer interaction; information visualization; computer vision..
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; computer hardware; computer systems; computer vision; databases; deep learning; e
版次1
doihttps://doi.org/10.1007/978-3-030-94893-1
isbn_softcover978-3-030-94892-4
isbn_ebook978-3-030-94893-1Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2022
The information of publication is updating

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Scene Text Localization Using Lightweight Convolutional Networksing the fusion of two lightweight neural network architectures, MobileNetV2 and Single Shot Detector (SSD), into our approach named MobText. As experimental results in the ICDAR’11 and ICDAR’13 datasets demonstrates that our solution yields the best trade-off between effectiveness and efficiency in
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Early Stopping for Two-Stream Fusion Applied to Action Recognitionual networks. In addition to reducing overfitting, this method also reduces the training cost, since it usually requires fewer epochs to complete the classification process. Experiments are conducted on UCF101 and HMDB51 datasets, which are two challenging benchmarks in the context of action recogni
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https://doi.org/10.1057/9781137325129rning to automatically construct projections from the extracted features. We show that our three variations of deep learning, which jointly treat feature extraction, selection, and projection, allow efficient creation of high-quality visual overviews of large shape collections, require minimal user
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https://doi.org/10.1057/9781403983213 humanities may improve and support the daily workflows of humanists. Despite the generally low level of digitization, multiple projects already build a basis to help in digitizing the field. As an example, the . project collected a vast amount of musicological data throughout the last 16 years and
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