Computational vision and regularization theory

Descriptions of physical properties of visible surfaces, such as their distance and the presence of edges, must be recovered from the primary image data and algorithms and parallel analog circuits that can solve ‘ill-posed problems’ and which are suggestive of neural equivalents in the brain are proposed.

Abstract

Descriptions of physical properties of visible surfaces, such as their distance and the presence of edges, must be recovered from the primary image data. Computational vision aims to understand how such descriptions can be obtained from inherently ambiguous and noisy data. A recent development in this field sees early vision as a set of ill-posed problems, which can be solved by the use of regularization methods. These lead to algorithms and parallel analog circuits that can solve 'ill-posed problems' and which are suggestive of neural equivalents in the brain.