Neural Dynamics of Perceptual Grouping: Textures, Boundaries, And Emergent Segmentations

Abstract

A real-time visual processing theory is used to analyse and explain a wide variety of perceptual grouping and segmentation phenomena, including the grouping of textured images, randomly defined images, and images built up from periodic scenic elements. The theory explains how “local” feature processing and “emergent” features work together to segment a scene, how segmentations may arise across image regions which do not contain any luminance differences, how segmentations may override local image properties in favor of global statistical factors, and why segmentations that powerfully influence object recognition may be barely visible or totally invisible. Network interactions within a Boundary Contour System (BCS), a Feature Contour System (FCS), and an Object Recognition System (ORS) are used to explain these phenomena. The BCS is defined by a hierarchy of orientationally tuned interactions, which can be divided into two successive subsystems, called the OC Filter and the CC Loop. The OC Filter contains two successive stages of oriented receptive fields which are sensitive to different properties of image contrasts. The OC Filter generates inputs to the CC Loop, which contains successive stages of spatially short-range competitive interactions and spatially long-range cooperative interactions. Feedback between the competitive and cooperative stages synthesizes a global context-sensitive segmentation from among the many possible groupings of local featural elements. The properties of the BCS provide a unified explanation of several ostensibly different Gestalt rules. The BCS also suggests explanations and predictions concerning the architecture of the striate and prestriate visual cortices. The BCS embodies new ideas concerning the foundations of geometry, on-line statistical decision theory, and the resolution of uncertainty in quantum measurement systems. Computer simulations establish the formal competence of the BCS as a perceptual grouping system. The properties of the BCS are compared with probabilistic and artificial intelligence models of segmentation. The total network suggests a new approach to the design of computer vision systems, and promises to provide a universal set of rules for perceptual grouping of scenic edges, textures, and smoothly shaded regions.