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

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书目名称Energy Minimization Methods in Computer Vision and Pattern Recognition影响因子(影响力)




书目名称Energy Minimization Methods in Computer Vision and Pattern Recognition影响因子(影响力)学科排名




书目名称Energy Minimization Methods in Computer Vision and Pattern Recognition网络公开度




书目名称Energy Minimization Methods in Computer Vision and Pattern Recognition网络公开度学科排名




书目名称Energy Minimization Methods in Computer Vision and Pattern Recognition被引频次




书目名称Energy Minimization Methods in Computer Vision and Pattern Recognition被引频次学科排名




书目名称Energy Minimization Methods in Computer Vision and Pattern Recognition年度引用




书目名称Energy Minimization Methods in Computer Vision and Pattern Recognition年度引用学科排名




书目名称Energy Minimization Methods in Computer Vision and Pattern Recognition读者反馈




书目名称Energy Minimization Methods in Computer Vision and Pattern Recognition读者反馈学科排名

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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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