Abstract
The problem of computing image texture features in parallel computers is addressed. It is shown that R. Haralick's texture measures (1973) are amenable to efficient implementation in certain fine-grained architectures. The main operation used to compute these features is the SEND, also called Random Access Write, command. This command is efficiently implemented in a number of computers, such as binary n-cubes, mesh-arrays, and some shared-memory systems. It is 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. >
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