Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Review
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Coherence-Enhancing Diffusion Filtering
International Journal of Computer Vision
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
The Nonlinear Statistics of High-Contrast Patches in Natural Images
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Texture Recognition Using a Non-Parametric Multi-Scale Statistical Model
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Improved Fast Gauss Transform and Efficient Kernel Density Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Unsupervised Non-parametric Region Segmentation Using Level Sets
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Unsupervised, Information-Theoretic, Adaptive Image Filtering for Image Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Threshold dynamics for the piecewise constant Mumford-Shah functional
Journal of Computational Physics
Cluster-based probability model and its application to image and texture processing
IEEE Transactions on Image Processing
Topological estimation using witness complexes
SPBG'04 Proceedings of the First Eurographics conference on Point-Based Graphics
Pattern Recognition Letters
A Statistical Overlap Prior for Variational Image Segmentation
International Journal of Computer Vision
Principal neighborhood dictionaries for nonlocal means image denoising
IEEE Transactions on Image Processing
segmenting multiple textured objects using geodesic active contour and DWT
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Variational region-based segmentation using multiple texture statistics
IEEE Transactions on Image Processing
Texture regimes for entropy-based multiscale image analysis
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
A general system for automatic biomedical image segmentation using intensity neighborhoods
Journal of Biomedical Imaging
A probabilistic multi-phase model for variational image segmentation
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Pattern Recognition Letters
Exploiting intensity inhomogeneity to extract textured objects from natural scenes
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Entropy-Scale profiles for texture segmentation
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Extracting the essence from sets of images
Computational Aesthetics'07 Proceedings of the Third Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
A new level set method for inhomogeneous image segmentation
Image and Vision Computing
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This paper presents a novel approach to unsupervised texture segmentation that relies on a very general nonparametric statistical model of image neighborhoods. The method models image neighborhoods directly, without the construction of intermediate features. It does not rely on using specific descriptors that work for certain kinds of textures, but is rather based on a more generic approach that tries to adaptively capture the core properties of textures. It exploits the fundamental description of textures as images derived from stationary random fields and models the associated higher-order statistics nonparametrically. This general formulation enables the method to easily adapt to various kinds of textures. The method minimizes an entropy-based metric on the probability density functions of image neighborhoods to give an optimal segmentation. The entropy minimization drives a very fast level-set scheme that uses threshold dynamics, which allows for a very rapid evolution towards the optimal segmentation during the initial iterations. The method does not rely on a training stage and, hence, is unsupervised. It automatically tunes its important internal parameters based on the information content of the data. The method generalizes in a straightforward manner from the two-region case to an arbitrary number of regions and incorporates an efficient multi-phase level-set framework. This paper presents numerous results, for both the two-texture and multiple-texture cases, using synthetic and real images that include electron-microscopy images.