The sum and difference of two random variables with same variances are decorrelated and define the principal axes of their associated joint probability function. Therefore, sum and difference histograms are introduced as an alternative to the usual co-occurrence matrices used for texture analysis. Two maximum likelihood texture classifiers are presented depending on the type of object used for texture characterization (sum and difference histograms or some associated global measures). Experimental results indicate that sum and difference histograms used conjointly are nearly as powerful as cooccurrence matrices for texture discrimination. The advantage of the proposed texture analysis method over the conventional spatial gray level dependence method is the decrease in computation time and memory storage.
IEEE Trans. Pattern Anal. Mach. Intell.
Classification,co-occurrence matrices,image processing,texture
Histogram,Computer vision,Random variable,Joint probability distribution,Pattern recognition,Computer science,Matrix (mathematics),Principal axis theorem,Image processing,Artificial intelligence,Probability density function,Computation