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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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(xué)科排名




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




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

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IST

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.