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Titlebook: Computer Vision – ACCV 2016 Workshops; ACCV 2016 Internatio Chu-Song Chen,Jiwen Lu,Kai-Kuang Ma Conference proceedings 2017 Springer Intern

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樓主: KEN
21#
發(fā)表于 2025-3-25 03:26:51 | 只看該作者
0302-9743 papers presented at 17 workshops held in conjunction with the 13th Asian Conference on Computer Vision, ACCV 2016, in Taipei, Taiwan in November 2016. The 134 full papers presented were selected from 223 submissions. LNCS 10116 contains the papers selected?978-3-319-54406-9978-3-319-54407-6Series IS
22#
發(fā)表于 2025-3-25 10:14:17 | 只看該作者
Conference proceedings 2017njunction with the 13th Asian Conference on Computer Vision, ACCV 2016, in Taipei, Taiwan in November 2016. The 134 full papers presented were selected from 223 submissions. LNCS 10116 contains the papers selected?
23#
發(fā)表于 2025-3-25 12:35:58 | 只看該作者
24#
發(fā)表于 2025-3-25 16:50:18 | 只看該作者
25#
發(fā)表于 2025-3-25 21:58:41 | 只看該作者
26#
發(fā)表于 2025-3-26 02:31:03 | 只看該作者
https://doi.org/10.1007/978-3-030-11815-0d in the low-rank recovery process. Furthermore, we propose an efficient algorithm based on the method of Alternating Direction Method of Multipliers (ADMM) for the non-convex optimization problem. The extensive experiments on the artificial data sets and real image data sets show the superiority of our method in image alignment and denoising.
27#
發(fā)表于 2025-3-26 07:45:35 | 只看該作者
Neuropeptides and Diabetic Wound-Healinghitecture to address both image denoising and single image super-resolution. Our 3DCF method achieves substantial improvements (0.1?dB–0.4?dB PSNR) over the state-of-the-art methods that it fuses on standard benchmarks for both tasks. At the same time, the method still is computationally efficient.
28#
發(fā)表于 2025-3-26 09:20:46 | 只看該作者
29#
發(fā)表于 2025-3-26 16:39:12 | 只看該作者
Mary G. Hochman,Caitlin Connollyicits of AD patients. This work uses novel EEG features based on quaternions, facial landmarks and the combination of them. Their performance was evaluated in a comparative study with a state of the art methods that demonstrates the proposed approach.
30#
發(fā)表于 2025-3-26 20:35:20 | 只看該作者
Single Image Dehazing Using Fixed Points and Nearest-Neighbor Regularizationization to recover a smooth transmission map. Compared with the state-of-the-art single image dehazing methods, the experimental results on some typical and challenged images demonstrate that our method can produce a high-quality dehazed image and recover the fine detail information and vivid color from the image haze regions.
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