Theory of edge perception for computer vision feedback control

Abstract

In this paper, the authors address the problem of edge-perception for its applications to vision-feedback control in robotic systems.

In natural vision, the recognition of objects takes place through the process consisting of ‘eye system’, ’neural networks’ and ‘cognition’. The cognitive process, in turn yields a phenomenon known as perception. This is the phenomenon of perception of physical attributes, such as edges, color and texture, etc., which is responsible for the recognition of objects through the natural vision processes.

In this paper, we make an attempt to postulate the theory of perception for gray-level images. The gray-level images, when going through the cognitive and perception processes, are contaminated by the uncertainty; here we call it ‘cognitive uncertainty’.

The studies in this paper are confined to the phenomenon of edge-perception for two-dimensional gray-level images, however, these studies can be extended to other types of visual attributes both in two-dimensional and three-dimensional spaces. Indeed, the perception of these attributes, which attempts to emulate the human vision system, may help in the design of a truly robust computer vision-feedback control system for robotic applications.