Introduction

This paper describes a class of computational techniques designed for the rapid detection and description of global features in a complex image – for example, detection of a long smooth curve on a background of shorter curves (Fig. 37.1).

Humans can perform such detection tasks in a fraction of a second; the curve ‘pops out’ of the display relatively immediately. In fact, the time required for a human to detect the curve is long enough for at most a few hundred neural firings – or, in computing terms, at most a few hundred ‘cycles’ of the neural ‘hardware’. If we regard the visual system as performing computations on the retinal image(s), with (sets of) neuron firings playing the role of basic operations, then human global feature detection performance implies that there must exist computational methods of global feature detection that take only a few hundred cycles.

Conventional computational techniques of image analysis fall far short of this level of performance. Parallel processing provides a possible approach to speeding up the computation; but some computations are not easy to speed up. For example, suppose we input the image into a two-dimensional array of processors, one pixel per processor, where each processor is connected to its neighbors in the array; this is a very natural type of ‘massive parallelism’ to use in processing images.