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Titlebook: Image and Graphics; 10th International C Yao Zhao,Nick Barnes,Chunyu Lin Conference proceedings 2019 Springer Nature Switzerland AG 2019 ar

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41#
發(fā)表于 2025-3-28 18:00:31 | 只看該作者
Kai Xue,Yiguang Liu,Weijie Hong,Qing Chang,Wenjuan Miaoe same time, huge regional differences have emerged. In this chapter, we identify three regions (eastern, central and western areas) in China based on four indicators of urbanization (regional development, urban-rural links, urban spatial distributions and urban function partitions). Then we conduct
42#
發(fā)表于 2025-3-28 21:20:18 | 只看該作者
43#
發(fā)表于 2025-3-29 01:43:42 | 只看該作者
AVE-WLS Method for Lossless Image Codingts introduced, e.g. its main stage is followed by a two-step NLMS predictor. Prediction error is coded by a completely new highly efficient binary context arithmetic coder. Its performance is compared with that of other coders for a set of widely used benchmark images.
44#
發(fā)表于 2025-3-29 04:07:31 | 只看該作者
0302-9743 on advances of theory, techniques and algorithms as well as innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking..978-3-030-34112-1978-3-030-34113-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
45#
發(fā)表于 2025-3-29 11:17:31 | 只看該作者
46#
發(fā)表于 2025-3-29 14:18:43 | 只看該作者
Conference proceedings 2019and Graphics, ICIG 2019, held in Beijing, China, in August 2019. The 183 full papers presented were selected from 384 submissions and focus on advances of theory, techniques and algorithms as well as innovative technologies of image, video and graphics processing and fostering innovation, entreprene
47#
發(fā)表于 2025-3-29 18:43:19 | 只看該作者
48#
發(fā)表于 2025-3-29 22:56:00 | 只看該作者
Common Subspace Based Low-Rank and Joint Sparse Representation for Multi-view Face Recognitionto learn and then fuse the discriminative features. Experiments on multi-view face dataset demonstrate that CSLRJSR outperforms the state-of-the-art methods both in two-view and multi-view situations.
49#
發(fā)表于 2025-3-30 02:56:51 | 只看該作者
50#
發(fā)表于 2025-3-30 04:16:24 | 只看該作者
An Improved Clustering Method for Multi-view Imagesor the multi-view clustering process. Experiments are performed using multiple datasets. The results show that the proposed algorithm is capable of multi-view clustering of image data with high accuracy, low error rate and favorable stability.
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