Abstract
The discrimination of texture features in an image has many important applications, from detection of man-made objects from a surrounding natural background to identification of cancerous from healthy tissue in x-ray imagery. The fractal structure in an image has been used with success to identify these features but requires unacceptable processing time if executed sequentially. We demonstrate a paradigm for applying massively parallel processing to the computation of fractal dimension of an image which will provide the necessary throughput demanded of real time applications. This model is evaluated on several architectures: Vectorizing Supercomputer, MIMD, and massively parallel SIMD computers. Performance comparisons are presented.
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