The Emerging Paradigm of Computational Vision

Abstract

Vision is the process by which structure in the world is inferred from images. But as such it is underconstrained; somehow additional constraints must be used to guide the inferences from two-dimensional (2-D) image data to three-dimensional (3-D) scene descriptions. The discovery of such constraints, and of techniques for using them, has led to the emergence of a new discipline called computational vision. It lies somewhere between the more classical attempts to solve engineering problems involving vision (as they arise, say, in robotics) and attempts to understand biological vision systems. Engineering applications suggest what should be solved; its scien­ tific counterparts suggest why; and biology suggests particular mechanisms that might be employed. But computational theories suggest how solutions can be obtained for different classes of visual tasks in the abstract, thereby filling the gap between particular problems and particular solutions. In this chapter I try to characterize the emergence of computational vision as its own discipline, contributing fundamental ideas to the study of both biological and engineered vision systems, rather than just supporting them with programming tools for testing existing ideas (cf Barlow et al 1972; Beck et al 1983; Braddick & Sleigh 1983). To illustrate the idea behind computational vision, consider an analogy with timekeeping. Mechanistic constraints come from clocks (the material from which they are composed, how they store energy, the mechanics of