Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Level Set Based Segmentation with Intensity and Curvature Priors
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
2D-Shape Analysis Using Conformal Mapping
International Journal of Computer Vision
Shape Registration in Implicit Spaces Using Information Theory and Free Form Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic tracking in joint feature-spatial spaces
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Active contours for tracking distributions
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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This paper deals with region-of-interest (ROI) segmentation in video sequences. The goal is to determine in one frame the region which best matches, in terms of a similarity measure, a ROI defined in a reference frame. A similarity measure can combine color histograms and geometry information into a joint PDF. Geometric information are basically interior region coordinates. We propose a system of shape coordinates constant under shape deformations. High-dimensional color-geometry PDF estimation being a difficult problem, measures based on these PDF distances may lead to an incorrect match. Instead, we use an estimator for Kullback-Leibler divergence efficient for high dimensional PDFs. The distance is expressed from the samples using the kth-nearest neighbor framework (kNN). We plugged this distance into active contour framework using shape derivative. Segmentation results on both rigid and articulated objects showed promising results.