Satellite Image Segmentation Using Wavelet Transforms Based on Color and Texture Features
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Analyzing image texture from blobs perspective
Proceedings of the 2005 joint Chinese-German conference on Cognitive systems
A comparative study of texture coarseness measures
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Remote sensing image fusion based on adaptive RBF neural network
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Texture Description Through Histograms of Equivalent Patterns
Journal of Mathematical Imaging and Vision
International Journal of Computer Applications in Technology
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In this paper we compare some of the traditional, and some fairly new techniques of texture analysis on the MeasTex and VisTex benchmarks to illustrate their relative abilities. The methods considered include autocorrelation (ACF), co-occurrence matrices (CM), edge frequency (EF), Law's masks (LM), run length (RL), binary stack method (BSM), texture operators (TO ), and texture spectrum (TS). In addition, we illustrate the advantage of using feature selection on a combined set that improves the overall recognition performance.