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Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; Third International Mário Figueiredo,Josiane Zerubia,Anil K. Jain

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樓主: DUCT
41#
發(fā)表于 2025-3-28 17:29:39 | 只看該作者
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發(fā)表于 2025-3-28 20:13:25 | 只看該作者
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發(fā)表于 2025-3-29 01:10:57 | 只看該作者
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發(fā)表于 2025-3-29 07:09:53 | 只看該作者
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發(fā)表于 2025-3-29 10:19:39 | 只看該作者
Spontaneous Order and the Utopian Collectiveion allows to merge HMM states in order to obtain a minimal set that do not significantly affect model performances. The approach has been tested on DNA sequence modeling and 2D shape classification. Results are presented in function of reduction rates, classification performances, and noise sensitivity.
46#
發(fā)表于 2025-3-29 11:23:44 | 只看該作者
M. Kozhevnikov,V. Narayanamurti energy functional with simulated annealing. Results with both synthetic and real stereo pairs demonstrate the improvement over the original SMW algorithm, which was already proven to perform better than state-of-the-art algorithms.
47#
發(fā)表于 2025-3-29 16:24:33 | 只看該作者
https://doi.org/10.1007/978-3-642-72366-7rence system, they are the same from different cameras, so more cameras are easily added, increasing the constraints over the same number of variables. Several successful experiments are presented for an arm motion and a leg motion from two and three cameras.
48#
發(fā)表于 2025-3-29 22:20:34 | 只看該作者
49#
發(fā)表于 2025-3-30 01:53:33 | 只看該作者
Antje Klinge Prof. Dr.,Mechthild Schütte Dr.iterion considers two objects as similar if there exists a mediating intra cluster path without an edge with large cost. The cost function is optimized in a multi-scale fashion. This new path based clustering concept is applied to segment textured images with strong texture gradients based on dissimilarities between image patches.
50#
發(fā)表于 2025-3-30 04:29:10 | 只看該作者
Maximum Likelihood Estimation of the Template of a Rigid Moving Objecterived as a computationally simple approximation to the . estimate of the parameters involved in the image sequence model: the motions, the template of the moving object, its intensity levels, and the intensity levels of the background pixels. We describe experiments that demonstrate the good performance of our algorithm.
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