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Titlebook: Image and Graphics; 8th International Co Yu-Jin Zhang Conference proceedings 2015 Springer Nature Switzerland AG 2015 3D animation.big data

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發(fā)表于 2025-3-21 19:20:51 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Image and Graphics
副標(biāo)題8th International Co
編輯Yu-Jin Zhang
視頻videohttp://file.papertrans.cn/462/461489/461489.mp4
概述Includes supplementary material:
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Image and Graphics; 8th International Co Yu-Jin Zhang Conference proceedings 2015 Springer Nature Switzerland AG 2015 3D animation.big data
描述This book constitutes the refereed conference proceedings of the 8th International Conference on Image and Graphics, ICIG 2015 held in Tianjin, China, in August 2015.The 164 revised full papers and 6 special issue papers were carefully reviewed and selected from 339 submissions. The papers focus on various advances of theory, techniques and algorithms in the fields of images and graphics.
出版日期Conference proceedings 2015
關(guān)鍵詞3D animation; big data; cloud computing; computational geometry; computer graphics; computer vision; data
版次1
doihttps://doi.org/10.1007/978-3-319-21963-9
isbn_softcover978-3-319-21962-2
isbn_ebook978-3-319-21963-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2015
The information of publication is updating

書(shū)目名稱(chēng)Image and Graphics影響因子(影響力)




書(shū)目名稱(chēng)Image and Graphics影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Image and Graphics網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Image and Graphics網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Image and Graphics被引頻次




書(shū)目名稱(chēng)Image and Graphics被引頻次學(xué)科排名




書(shū)目名稱(chēng)Image and Graphics年度引用




書(shū)目名稱(chēng)Image and Graphics年度引用學(xué)科排名




書(shū)目名稱(chēng)Image and Graphics讀者反饋




書(shū)目名稱(chēng)Image and Graphics讀者反饋學(xué)科排名




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Compressed Binary Discriminative Feature for Fast UAV Image Registration,omparing image gradients static information over a log-polar location grid pattern. Extensive evaluations on benchmark datasets and real-world UAV images show that CBDF yields a similar performance with SIFT and SURF, and it is much more efficient in terms of both computation time and memory.
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Contour-Based Plant Leaf Image Segmentation Using Visual Saliency,position. Furthermore, the proposed active model can segment images adaptively and automatically. Experiments on two applications demonstrate that the proposed model can achieve a better segmentation result.
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