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Titlebook: Computer Vision – ECCV 2012; 12th European Confer Andrew Fitzgibbon,Svetlana Lazebnik,Cordelia Schmi Conference proceedings 2012 Springer-V

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發(fā)表于 2025-3-23 10:31:05 | 只看該作者
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發(fā)表于 2025-3-23 15:00:55 | 只看該作者
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發(fā)表于 2025-3-23 20:09:42 | 只看該作者
Grain Segmentation of 3D Superalloy Images Using Multichannel EWCVT under Human Annotation Constrainsolve this constrained minimization problem. In particular, manually annotated segmentation on a very small set of 2D slices are taken as constraints and incorporated into the whole clustering process. Experimental results demonstrate that the proposed CMEWCVT algorithm significantly improve the pre
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發(fā)表于 2025-3-24 00:17:09 | 只看該作者
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發(fā)表于 2025-3-24 04:13:32 | 只看該作者
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發(fā)表于 2025-3-24 09:05:51 | 只看該作者
,Pragmatics and Reviewers’ Reports,an polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein and the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer’s study.
17#
發(fā)表于 2025-3-24 12:29:48 | 只看該作者
,Pragmatics and Reviewers’ Reports, employ approximate nearest neighbor search to speed-up the E-step and exploit its iterative nature to make search incremental, boosting both speed and precision. We achieve superior performance in large scale retrieval, being as fast as the best known approximate .-means.
18#
發(fā)表于 2025-3-24 18:39:15 | 只看該作者
19#
發(fā)表于 2025-3-24 21:11:02 | 只看該作者
Polynomial Regression on Riemannian Manifoldsan polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein and the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer’s study.
20#
發(fā)表于 2025-3-25 02:26:34 | 只看該作者
Approximate Gaussian Mixtures for Large Scale Vocabularies employ approximate nearest neighbor search to speed-up the E-step and exploit its iterative nature to make search incremental, boosting both speed and precision. We achieve superior performance in large scale retrieval, being as fast as the best known approximate .-means.
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