Abstract

Methods are developed that permit the decomposition of two-dimensional convolutional kernels defined on a large area of support into sets of 1 × 3 and 3 × 1 convolutional primitives that are the natural tools of high-speed parallel processors having limited neighborhood data access. Five primitives are shown to be sufficient for 4-neighbor architectures with an additional four primitives for 8-neighbor architectures. Examples of the decomposition process are described for several low-level image analysis kernels, and it is shown that spectral analysis and edge detection can be performed over a wide range of scale. This leads to the conjecture that the convolutional primitives introduced here may be the parallel to the barlike receptors often cited as a model of human visual processing in that both may permit complex processes, such as spectral analysis, to be affected by the massive concatenation of simple primitives.

© 1988 Optical Society of America

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