Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; Second International Edwin R. Hancock,Marcello Pelillo Conference p

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書目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition影響因子(影響力)




書目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition影響因子(影響力)學科排名




書目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition網(wǎng)絡公開度




書目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition網(wǎng)絡公開度學科排名




書目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition被引頻次




書目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition被引頻次學科排名




書目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition年度引用




書目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition年度引用學科排名




書目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition讀者反饋




書目名稱Energy Minimization Methods in Computer Vision and Pattern Recognition讀者反饋學科排名

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https://doi.org/10.1007/978-3-030-61326-6nce the stability of the DMM and improve the performance of the adaptive process. The accuracy of the proposed approach is demonstrated by experiments on eye model animation. In this paper, we focus our discussion only on the detection, tracking, modeling and animation of eye movements.

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Unsupervised Multispectral Image Segmentation Using Generalized Gaussian Noise Modeles of Posterior Marginals (MPM) estimator. The main motivation of the paper is to extend the variety of noise models which results of the distribution mixture on multispectral images. Some results on synthetic and SPOT images validate our approach.

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