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Titlebook: Advances in Multimedia Information Processing – PCM 2017; 18th Pacific-Rim Con Bing Zeng,Qingming Huang,Xiaopeng Fan Conference proceedings

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樓主: Roosevelt
31#
發(fā)表于 2025-3-27 00:14:40 | 只看該作者
32#
發(fā)表于 2025-3-27 01:55:44 | 只看該作者
Introduction to Steady-State Systemsal prior of random walk model by adding an extra item into the weight matrix of the graph constructed from an image. Experimental results show that the proposed method acts better than Dense CRF in pixel accuracy and mean IoU, and obtains smoother results. In addition, our method significantly reduces the time cost of refinement process.
33#
發(fā)表于 2025-3-27 08:43:27 | 只看該作者
Cooperativity Theory in Biochemistrynce image using the dense motion fields of background and reflection respectively. Finally, with the initial solution provided, the background and reflection images can be separated using alternating optimization. Experiment results showed that our method can achieve a robust performance compared with the state of art.
34#
發(fā)表于 2025-3-27 09:27:33 | 只看該作者
Cooperativity Theory in Biochemistrycale local appearance based keypoint likelihood with filtered viewpoint conditioned likelihood to induce a considerable performance gain. Experimentally, we show that our framework outperforms state-of-the-art methods on PASCAL 3D benchmark.
35#
發(fā)表于 2025-3-27 17:24:47 | 只看該作者
https://doi.org/10.1007/978-1-4757-3302-0rallelism method to accelerate constraint resolving and collision detection. As a result, our system can provide realistic effects for the virtual fitting while meeting the real-time and robustness requirements.
36#
發(fā)表于 2025-3-27 20:06:12 | 只看該作者
37#
發(fā)表于 2025-3-28 01:30:47 | 只看該作者
38#
發(fā)表于 2025-3-28 02:37:52 | 只看該作者
39#
發(fā)表于 2025-3-28 07:06:55 | 只看該作者
40#
發(fā)表于 2025-3-28 12:26:10 | 只看該作者
A Fine-Grained Filtered Viewpoint Informed Keypoint Prediction from 2D Imagescale local appearance based keypoint likelihood with filtered viewpoint conditioned likelihood to induce a considerable performance gain. Experimentally, we show that our framework outperforms state-of-the-art methods on PASCAL 3D benchmark.
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