Transputer multi-processing and other topics in machine vision
Thesis DisciplineElectrical Engineering
Degree GrantorUniversity of Canterbury
Degree NameDoctor of Philosophy
This thesis is concerned with machine vision including its application to the task of detecting surface blemishes and shape defects in kiwifruit at a rate of four fruit per second. Existing machine vision technology is subject to the twin constraints of large data volumes and restricted processing time. Two approaches to this problem are explored: the use of large processing power, and the reduction of the data volume. The provision of large processing power is achieved through the use of networks containing large numbers of micro-processors. The establishment of a Transputer Image Processing System (TIPS) has provided a test facility for the development of algorithms on a multitransputer system. In particular, distributed versions of the convex hull algorithm and of algorithms for image translation and rotation have been developed. The establishment of TIPS required the development of a shell to provide protection against deadlock and to provide a satisfactory environment for software development. The network topology is a significant factor in the system performance, and a particular network, the degree four chordal ring network, is proposed as a suitable network for transputer-based systems. The manner in which image processing operations map onto a multi-processor is also investigated. The alternative approach to a practical machine vision system is to decrease the volume of image data. This can be achieved using pyramidal vision, and the approach explored in this thesis uses a rank-based technique for the formation of each subsequent layer of the pyramid. In particular, a rank of one or two results in darker blemishes being emphasized relative to their surroundings. As a consequence, the volume of image data required to preserve blemish information is very much reduced. Another aspect of machine vision is lighting, and the problem of determining the optimum form of lighting for blemish detection on kiwifruit is explored. A machine vision system based on a combination of pyramidal vision and multi-transputer networks is proposed.