Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Elements of information theory
Elements of information theory
Region-based strategies for active contour models
International Journal of Computer Vision
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
A Statistical Approach to Snakes for Bimodal and Trimodal Imagery
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Variational Space-Time Motion Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Segmentation of a Vector Field: Dominant Parameter and Shape Optimization
Journal of Mathematical Imaging and Vision
A 3-step algorithm using region-based active contours for video objects detection
EURASIP Journal on Applied Signal Processing
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Robust real-time segmentation of images and videos using a smooth-spline snake-based algorithm
IEEE Transactions on Image Processing
Nonparametric Level-Set Segmentation Based on the Minimization of the Stochastic Complexity
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Local Histogram Based Segmentation Using the Wasserstein Distance
International Journal of Computer Vision
High-dimensional statistical measure for region-of-interest tracking
IEEE Transactions on Image Processing
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Unsupervised texture segmentation using active contours driven by the Chernoff gradient flow
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Texture segmentation via non-local non-parametric active contours
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Fast finsler active contours and shape prior descriptor
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Entropy-Scale profiles for texture segmentation
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
A robust patch-statistical active contour model for image segmentation
Pattern Recognition Letters
Tensor-SIFT Based Earth Mover's Distance for Contour Tracking
Journal of Mathematical Imaging and Vision
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In this paper, we propose to focus on the segmentation of vectorial features (e.g. vector fields or color intensity) using region-based active contours. We search for a domain that minimizes a criterion based on homogeneity measures of the vectorial features. We choose to evaluate, within each region to be segmented, the average quantity of information carried out by the vectorial features, namely the joint entropy of vector components. We do not make any assumption on the underlying distribution of joint probability density functions of vector components, and so we evaluate the entropy using non parametric probability density functions. A local shape minimizer is then obtained through the evolution of a deformable domain in the direction of the shape gradient.The first contribution of this paper lies in the methodological approach used to differentiate such a criterion. This approach is mainly based on shape optimization tools. The second one is the extension of this method to vectorial data. We apply this segmentation method on color images for the segmentation of color homogeneous regions. We then focus on the segmentation of synthetic vector fields and show interesting results where motion vector fields may be separated using both their length and their direction. Then, optical flow is estimated in real video sequences and segmented using the proposed technique. This leads to promising results for the segmentation of moving video objects.