This thesis focuses on the problems of using image processing for the task of identifying
amorphous objects in the food industry. To aid this investigation a system has been
constructed to identify amorphous overlapping objects where specific elements of the
image may be partially or completely obscured. Furthermore the elements may vary
greatly in shape and basic image properties.
The recognition system has a layered architecture. The low level layer consists of pattern
classifiers using colour and texture. At the higher layers a rule based system uses domain
specific knowledge to uniquely label the image elements that, due to the high variability
of their basic image properties cannot be identified by the lower layers.
The system is applied to the specific task of recognising certain key elements within the
viscera of a bovine carcass varying in weight from 200-800kg (and associated breed, sex
and age differences) previously automatically ejected onto a conveyor from the ventral
cavity of the animal.
The performance achieved is similar to that of a human working with the same images.
The thesis argues that there is an intrinsic use of knowledge with image processing
solutions to achieve this level of performance and at every stage of processing
knowledge must be incorporated into the system.
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.