A Parallel Approach for Statistical Texture Parameter Calculation

Abstract

This chapter focusses on the development of a new image processing technique for the processing of large and complex images, especially SAR images. We propose here a new and effective approach that outperforms the existing methods for the calculation of high order textural parameters. With a single processor, this approach is about $256^{n-1}$ times faster than the co-occurrence matrix approach considered as classical, where $n$ is the order of the textural parameter for a 256-gray scales image. In a parallel environment made of N processor, this performance can almost be multiply by the factor N. Our approach is based on a new modeling of textural parameters of a generic order $n>1$ equivalent to the classical formulation, but which is no longer based on the co-occurrence matrix of order $n>1$. By avoiding the calculation of the co-occurrence matrix of order $n>1$, the resulted model enables a gain of about $256^{n}$ bytes of the required memory space.