Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
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
A fast level set method for propagating interfaces
Journal of Computational Physics
The nature of statistical learning theory
The nature of statistical learning theory
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
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
IEEE Transactions on Image Processing
Toward a generic evaluation of image segmentation
IEEE Transactions on Image Processing
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In this paper, we propose a new active contour model for supervised texture segmentation driven by a binary classifier instead of a standard motion equation. A recent level set implementation developed by Shi et al in [1] is employed in an original way to introduce the classifier in the active contour. Carried out on a learning image, an expert segmentation is used to build the learning dataset composed of samples defined by their Haralick texture features. Then, the pre-learned classifier is used to drive the active contour among several test images. Results of three active contours driven by binary classifiers are presented: a k-nearest-neighbors model, a support vector machine model and a neural network model. Results are presented on medical echographic images and remote sensing images and compared to the Chan-Vese region-based active contour in terms of accuracy, bringing out the high performances of the proposed models.