TEXTURE ANALYSIS USING GRAY LEVEL GAP LENGTH MATRIX

Several texture features are introduced from a proposed higher-order statistical matrix, the gray level gap length matrix (GLGLM). The GLGLM measures the gray level variations in an image. This matrix can be seen as a complement to the gray level run length matrix (GLRLM). It offers size distribution of texture elements for a given direction in the image. We find that the feature set of the GLGLM gives good results for texture classification. For periodicity detection, features extracted from the GLGLM works much faster than the commonly used K parameters, and provides additional quasi periodicities. The method provides effective and efficient texture classification, periodicity detection and image granulometry. The approach opens a new and computationally efficient way of automatic texture analysis and synthesis.