Texture description and segmentation through fractal geometry
Computer Vision, Graphics, and Image Processing
Texture feature performance for image segmentation
Pattern Recognition
Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Gibbs Random Fields, Cooccurrences, and Texture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of several approaches for the segmentation of texture images
Pattern Recognition Letters
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Basic Gray Level Aura Matrices: Theory and its Application to Texture Synthesis
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
IEEE Transactions on Visualization and Computer Graphics
Dayside corona aurora classification based on X-gray level aura matrices
Proceedings of the ACM International Conference on Image and Video Retrieval
A robust automatic clustering scheme for image segmentation using wavelets
IEEE Transactions on Image Processing
Hi-index | 0.00 |
Inspired by an intuitive analogy that exists between the gray level textures and the miscibility in the multiphase fluids, the aura concept was developed from set theory tools in order to modeling the texture image. The gray level aura matrix (GLAM) has been then proposed to generalize the gray level cooccurrence matrix (GLCM) which remains very popular in the texture analysis. The GLAM indicates how much each gray level is present in the neighborhood of each other gray level. The neighborhood is defined by a structuring element as one used in mathematical morphology. The GLAM is mainly used and studied in synthesis and classification of textures framework but very few works are devoted to the segmentation. The aim of this paper is to exploit the GLAM for the segmentation of textured images. Experiments results over synthetic and real images show the efficiency of the GLAM. The influence of the shape and the size of the structuring element on the segmentation results are also studied.