EMMCVPR 2001 - PDF Preprint Papers

EMMCVPR 2001 International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition

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EMMCVPR
2001
Springer

338views Computer Vision» more EMMCVPR 2001»

3D Flux Maximizing Flows

13 years 9 months ago

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A number of geometric active contour and surface models have been proposed for shape segmentation in the literature. The essential idea is to evolve a curve (in 2D) or a surface (i...

Kaleem Siddiqi, Alexander Vasilevskiy

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Id	Views	Title	Status

1	338	3D Flux Maximizing Flows	claim
2	260	Geodesic Interpolating Splines	claim
3	207	Articulated Object Tracking via a Genetic Algorithm	claim
4	203	Estimation of Distribution Algorithms: A New Evolutionary Computation Approach for Graph Matching Problems	claim
5	202	A Hierarchical Markov Random Field Model for Figure-Ground Segregation	claim
6	176	An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision	claim
7	164	Grouping with Directed Relationships	claim
8	141	Multiple Contour Finding and Perceptual Grouping as a Set of Energy Minimizing Paths	claim
9	139	Path Based Pairwise Data Clustering with Application to Texture Segmentation	claim
10	139	Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics	claim
11	124	A Double-Loop Algorithm to Minimize the Bethe Free Energy	claim
12	123	Averaged Template Matching Equations	claim
13	122	Designing the Minimal Structure of Hidden Markov Model by Bisimulation	claim
14	112	Matching Images to Models - Camera Calibration for 3-D Surface Reconstruction	claim
15	105	Gabor Feature Space Diffusion via the Minimal Weighted Area Method	claim
16	91	Image Labeling and Grouping by Minimizing Linear Functionals over Cones	claim
17	89	Maximum Likelihood Estimation of the Template of a Rigid Moving Object	claim
18	85	Supervised Texture Segmentation by Maximising Conditional Likelihood	claim
19	79	A Discrete/Continuous Minimization Method in Interferometric Image Processing	claim
20	78	Relaxing Symmetric Multiple Windows Stereo Using Markov Random Fields	claim

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