Overview

Perceptual organization provides significant computational leverage and can do so over several layers of abstraction. The sophistication of a vision system, on the whole, lies largely in the sophistication of its perceptual organization processes. In biological systems, visual capability is developed to a degree appropriate to the ambulatory capabilities of the organism, i.e., the degree to which it can use the information to acquire food, elude danger, or otherwise restructure its environment. The performance of a machine vision system is coupled to the chosen problem domain, which is analogous. It is the recognition, development, and exploitation of perceptual organization concepts, models, paradigms, and computational techniques that brings efficiency to machine vision systems. Perceptual organization allows us to assign computational resources intelligently, which is also important in biological systems because of the relative expense of neural tissue from an evolutionary standpoint. As we shall see, perceptual organization in computer vision uses computational resources effectively to extract organizations from which features are hypothesized, instead of the expensive alternative of directly applying feature detectors over the entire image.