Computing Image Texture Features in Parallel Computers

Abstract

In this letter, the problem of computing image texture features in parallel computers is addressed. Specifically, it will be shown that Haralick's texture measures [1] are amenable to efficient implementation in certain fine-grained architectures. The main operation that will be used to compute these features is the SEND, also called Random Access Write, command. This command is efficiently implemented in a number of today's computers such as binary n-cubes, mesh-arrays, and some shared memory systems. In particular, it will be shown that the computation of gray-level dependency matrices requires random global communication patterns. This feature and the need for other standard local processing make the classification measures proposed by Haralick and his associates good candidates as benchmarks for parallel computer vision architectures. © 1988 IEEE