Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Model for Image Classification and Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level Set Model for Image Classification
International Journal of Computer Vision
Statistical active grid for segmentation refinement
Pattern Recognition Letters
A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Gradient Vector Flow Fast Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Watershed identification of polygonal patterns in noisy SAR images
IEEE Transactions on Image Processing
Minimum description length synthetic aperture radar image segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally adaptive region information for automatic color-texture image segmentation
Pattern Recognition Letters
Optical aerial image partitioning using level sets based on modified Chan-Vese model
Pattern Recognition Letters
Comparison of polarimetric SAR observables in terms of classification performance
International Journal of Remote Sensing
A Statistical Overlap Prior for Variational Image Segmentation
International Journal of Computer Vision
Level Set Image Segmentation with a Statistical Overlap Constraint
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Coastline Detection from SAR Images by Level Set Model
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Target detection in SAR images based on a level set approach
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
SAR Image Segmentation Using Level Sets and Region Competition under the $\mathcal{G}^H$ Model
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Classification of water regions in SAR images using level sets and non-parametric density estimation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multiregion level-set segmentation of synthetic aperture radar images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Image segmentation in a kernel-induced space
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Effective level set image segmentation with a kernel induced data term
IEEE Transactions on Image Processing
Smooth contour coding with minimal description length active grid segmentation techniques
Pattern Recognition Letters
Regions segmentation from SAR images
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Information Sciences: an International Journal
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The purpose of this study is to investigate Synthetic Aperture Radar (SAR) image segmentation into a given but arbitrary number of gamma homogeneous regions via active contours and level sets. The segmentation of SAR images is a difficult problem due to the presence of speckle which can be modeled as strong, multiplicative noise. The proposed algorithm consists of evolving simple closed planar curves within an explicit correspondence between the interiors of curves and regions of segmentation to minimize a criterion containing a term of conformity of data to a speckle model of noise and a term of regularization. Results are shown on both synthetic and real images.