Fusing multiple images and extracting features for visual inspection

The aim of image analysis is not to compress and summarise data from images. It is concerned to use image data to abstract information about scenes and objects. The research described in this paper seeks to use insights taken from biological models of the primate visual system to design reliable visual inspection procedures. It forms part of a programme designed to create a coherent image analysis toolbox of biologically inspired algorithms. The paper has both presented practical examples of balanced gradient kernels, and provided the general theory that constrains the point spread functions employed. Two fast methods for edge segment extraction that make use of both gradient and orientation information are able to parameterise edge segments, even down to one pixel in length. In addition to parametric information, confidence factors can also be calculated and incorporated into inspection decision making. These methods, although biologically inspired, are an attempt to code some aspects of neural function in a form efficient for serial computation