Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; 5th International Wo Anand Rangarajan,Baba Vemuri,Alan L. Yuille Co

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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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Optimizing the Cauchy-Schwarz PDF Distance for Information Theoretic, Non-parametric Clusteringemberships of the data patterns, in order to maximize the recent Cauchy-Schwarz (CS) probability density function (pdf) distance measure. Each pdf corresponds to a cluster. The CS distance is estimated analytically and non-parametrically by means of the Parzen window technique for density estimation

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Bayesian Image Segmentation Using Gaussian Field Priorsally discrete problem. Bayesian approaches to segmentation use priors to impose spatial coherence; the discrete nature of segmentation demands priors defined on discrete-valued fields, thus leading to difficult combinatorial problems..This paper presents a formulation which allows using continuous p

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Handling Missing Data in the Computation of 3D Affine Transformationsmanner have proven the most effective to deal with large image sequences. One of the key building blocks of these hierarchical approaches is the alignment of two partial 3D models, which requires to express them in the same 3D coordinate frame by computing a 3D transformation. This problem has been

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Deformable-Model Based Textured Object Segmentationces in traditional deformable models come primarily from edges or gradient information and it becomes problematic when the object surfaces have complex large-scale texture patterns that generate many local edges within a same region. We introduce a new textured object segmentation algorithm that has

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Total Variation Minimization and a Class of Binary MRF Modelsion approach to image denoising. We show, more precisely, that solutions to binary MRFs can be found by minimizing an appropriate ROF problem, and vice-versa. This leads to new algorithms. We then compare the efficiency of various algorithms.