Human image understanding: Recent research and a theory

Abstract

The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into simple volumetric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recognition-by-components (RBC), is that a modest set of components [ N probably ≤ 36] can be derived from contrasts of five readily detectable properties of edges in a 2-dimensional image: curvature, collinearity, symmetry, parallelism, and cotermination. The detection of these properties is generally invariant over viewing position and image quality and consequently allows robust object perception when the image is projected from a novel viewpoint or degraded. RBC thus provides a principled account of the heretofore undecided relation between the classic principles of perceptual organization and pattern recognition: The constraints toward regularization (Pragnanz) characterize not the complete object but the object's components. A principle of componential recovery can account for the major phenomena of object recognition: If an arrangement of two or three primitive components can be recovered from the input, objects can be quickly recognized even when they are occluded, rotated in depth, novel, or extensively degraded. The results from experiments on the perception of briefly presented pictures by human observers provide empirical support for the theory.