Computer vision using shape spaces / Burzin Bhavnagri.

Abstract

This thesis investigates a computational model of vision based on assumptions pertaining to the physical structure of a camera and the scattering of light from visible surfaces. A sufficient condition to detect occlusions, intensity discontinuities, discontinuities in derivatives of intensity, surface discontinuities and discontinuities in derivatives of surfaces are given. This leads to an algorithm with linear time and space complexity to generate a collection of feature points with attributes in cyclically ordered groups. Two approaches to rejecting false hypotheses of correspondence were developed: an error minimising approach and an approach based on formal language. A non-iterative algorithm that can use the rotation between two cameras to produce an exact reconstruction of a scene is presented. Two methods of comparing global shapes with occlusions are pointed out: one based on a grammar, the other on Le's inequality on euclidean shapes.