Texture discrimination by Gabor functions
Biological Cybernetics
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture segmentation using Gabor modulation/demodulation
Pattern Recognition Letters
Texture description and segmentation through fractal geometry
Computer Vision, Graphics, and Image Processing
Handbook of pattern recognition & computer vision
Moment-based texture segmentation
Pattern Recognition Letters
Extended fractal analysis for texture classification and segmentation
IEEE Transactions on Image Processing
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In this paper, we use a multifractal approach based on the computation of two spectrums for image analysis and texture segmentation problems. The two spectrums are the Legendre Spectrum, determined by classical methods, and the Large Deviation Spectrum, determined by kernel density estimation. We propose a way for the fusion of these two spectrums to improve textured image segmentation results. An unsupervised k-means is used as clustering approach for the texture classification. The algorithm is applied on mosaic image built using IKONOS images and various natural textures from the Brodatz album. The segmentation obtained with our approach gives better results than the application of each spectrum separately.