Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
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
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
Nonlinear Matrix Diffusion for Optic Flow Estimation
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Level Set Based Segmentation with Intensity and Curvature Priors
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
IEEE Transactions on Image Processing
International Journal of Computer Vision
A tensorial framework for color images
Pattern Recognition Letters
An active contours method based on intensity and reduced gabor features for texture segmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Hip joint segmentation from 2D ultrasound data based on dynamic shape priors
ICECS'05 Proceedings of the 4th WSEAS international conference on Electronics, control and signal processing
Computational framework for family of order statistic filters for tensor valued data
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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In this paper, we propose an original approach for texture and colour segmentation based on the tensor processing of the nonlinear structure tensor. While the tensor structure is a well established tool for image segmentation, its advantages were only partly used because of the vector processing of that information. In this work, we use more appropriate definitions of tensor distance grounded in concepts from information theory and compare their performance on a large number of images. We clearly show that the traditional Frobenius norm-based tensor distance is not the most appropriate one. Symmetrized KL divergence and Riemannian distance intrinsic to the manifold of the symmetric positive definite matrices are tested and compared. Adding to that, the extended structure tensor and the compact structure tensor are two new concepts that we present to incorporate gray or colour information without losing the tensor properties. The performance and the superiority of the Riemannian based approach over some recent studies are demonstrated on a large number of gray-level and colour data sets as well as real images.