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Robust toolbox for texture classification and segmentation

In this paper, the design and use of a toolbox that integrates several texture analysis algorithms is presented. The most important statistical, spectral and multiresolution methods are implemented. Examples of the toolbox interfaces are given. The interface windows for the algorithms and classifiers are explained. Experimental result are presented which show the application of the toolbox algorithms for image classification and segmentation. Textures that are transformed can also be classified, an example is presented using a wavelet algorithm Segmentation of remote sensing images is discussed using the co-occurrence matrix method. Classification with extrema features is demonstrated for different sets of images. An application of the algorithm to segmenting industrial images using logical transform algorithm is discussed. The organization of the toolbox is in a hierarchical manner. It also implements auxiliary methods such as edge detection and noise filtering that aid in texture analysis.