Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Robot Vision
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
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
Structure-Texture Image Decomposition--Modeling, Algorithms, and Parameter Selection
International Journal of Computer Vision
Total Variation Models for Variable Lighting Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Vectorial Image Features Using Shape Gradients and Information Measures
Journal of Mathematical Imaging and Vision
Fast Global Minimization of the Active Contour/Snake Model
Journal of Mathematical Imaging and Vision
Local Histogram Based Segmentation Using the Wasserstein Distance
International Journal of Computer Vision
Unsupervised texture segmentation with nonparametric neighborhood statistics
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Integrated active contours for texture segmentation
IEEE Transactions on Image Processing
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow
IEEE Transactions on Image Processing
Hi-index | 0.00 |
We propose a variational approach to unsupervised texture segmentation that depends on very few parameters and is robust to imaging conditions. First, the uneven illumination in the observed image is removed by the proposed image decomposition model that approximates the illumination and well retains the textures and features in the image. Then, from the obtained intrinsic image, we introduce a new data, multiscale local entropy, which is the entropy of each location's neighborhood histogram with various scales. The proposed segmentation model uses multiscale local entropy as data. Together with a length penalizing term, minimizing the energy functional locates the contours so that the local entropy within each region is similar to one another. Since entropy is the only feature, there are very few parameters. Moreover, the segmentation model can be solved by a fast global minimization method. Experimental results on natural images show the proposed method is able to robustly segment various texture patterns with uneven illumination in the original images.