Unsupervised texture segmentation using Gabor filters
Pattern Recognition
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CVGIP: Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geostatistical classification for remote sensing: an introduction
Computers & Geosciences
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification by multi-model feature integration using Bayesian networks
Pattern Recognition Letters
Texture segmentation using wavelet transform
Pattern Recognition Letters
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
An experiment on texture segmentation using modulated wavelets
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Texture periodicity detection: features, properties, and comparisons
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Inspired by Krige' variogram and the multi-channel filtering theory for human vision information processing, this paper proposes a novel algorithm for segmenting the textures based on experimental semi-variogram function (ESVF), which can simultaneously describe structural property and statistical property of textures. The single variogram function value (SVFV) and the variance distance obtained by ESVF are used as texture feature description for segmenting textures. The feasibility and effectiveness of the proposed method are demonstrated by testing on some texture images. The computational complexity of the proposed approach depends neither on the number of the textures nor on the number of the gray levels, and only on the size of the image blocks. We have proved theoretically that the algorithm has the advantages of direction invariability and a higher sensitivity to different textures and can detect almost all kinds of the boundaries of the shape textures. Experimental results on the Brodatz texture databases show that the performance of this algorithm is superior to the traditional techniques such as texture spectrum, SIFT, k-mean method, and Gabor filters. The proposed approach is found to be robust, efficient, and satisfactory.