Global image feature correspondence under a multi-image network

Abstract

This paper discusses a global image feature correspondence strategy under a multi-image network. The term multi-image network is used to describe such geometries, where every object point ‘node’ has more than two corresponding imaging rays associated with it. The term global image feature correspondence refers to all those image features that are matched simultaneously. The global image feature correspondence requires a global matching strategy without employing any one image as a fixed reference image. The primary characteristics of the work includes application of epipolar curve constraints, use of multi-ray triangulation residuals in object space, adoption of least-squares network optimisation and application of global quality control measures. The matching speed for object point determination in the reported multi-image network reconstruction implementation, in the case of four images, reaches 120 points per second using a Pentium-200 processor. A three-dimensional triangulation accuracy of close to 0.1 pixel is achieved.