Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; 4th International Wo Anand Rangarajan,Mário Figueiredo,Josiane Zeru

激勵(lì)

Curve Matching Using the Fast Marching Methodwe compare dissimilarity functions based on local curvature information and on shape contexts. We have tested the algorithm on a database of 110 sample curves by performing “best matches” experiments.

赦免

Tidious

Maximum Entropy Models for Skin Detections of the solution. The ROC curve obtained shows better performance than the baseline model. Finally, color gradient is included. Thanks to Bethe tree approximation, we obtain a simple analytical expression for the coefficients of the associated maximum entropy model. Performance, compared with previous model is once more improved.

LAST

征兵

Pr?vention durch k?rperliche Aktivit?to zero when the number of samples goes to infinity. This criterion is obtained by Large Deviation Theory techniques and characterizes globally the multi-hypothesis discrimination problem. An application on 2D rotation invariant shape recognition with non-closed noisy contours illustrates the approach.

改正

https://doi.org/10.1007/978-3-642-76858-3into this learning framework. The resulting optimization problem is solved by constrained Deterministic Annealing. The approach is illustrated for both artificial data and real-world synthetic aperture radar (SAR) imagery.

褪色

Information Force Clustering Using Directed Trees only it’s variance, as many traditional algorithms based on mere second order statistics rely on. We demonstrate the performance of our clustering technique when applied to both artificially created data and real data, and also discuss some limitations of the proposed method.

召集

Active Sampling Strategies for Multihypothesis Testingo zero when the number of samples goes to infinity. This criterion is obtained by Large Deviation Theory techniques and characterizes globally the multi-hypothesis discrimination problem. An application on 2D rotation invariant shape recognition with non-closed noisy contours illustrates the approach.

Fresco

Semi-supervised Image Segmentation by Parametric Distributional Clusteringinto this learning framework. The resulting optimization problem is solved by constrained Deterministic Annealing. The approach is illustrated for both artificial data and real-world synthetic aperture radar (SAR) imagery.

CARE

Hierarchical Annealing for Random Image Synthesisonal data may result in huge configuration spaces. In this paper a method of hierarchical simulated annealing is introduced, which can lead to large gains in computational complexity for suitable models. As an example, the approach is applied to the synthesis of binary porous media images.