Machine vision system developments for industrial inspection applications

Abstract

This thesis describes research in the area of automated industrial inspection using machine vision systems. It is anticipated that the algorithms described will contribute to the design of a machine vision system for the automatic surface inspection of cylindrical pellets.
Firstly, the acquisition and segmentation of pellet tray images using area capture is described. Individual pellets are segmented from a pellet tray image by a novel system using the Radon transform coupled with data clustering. Subsequent to the segmentation, the linking of four pellet views depicting the entire circumferential area of the pellet is described along with a simple technique to compensate for intensity variations brought about by imaging the three-dimensional cylindrical surface of the pellet.
The image processing techniques of filtering, edge detection, thresholding and morphology are used in the segmentation of grey level pellet defect images. The grey level pellet images are low-pass filtered and binary images formed using edge detection with thresholding. Binary morphology operators are then used in conjunction with a termination condition based on the number of objects in the image to ensure homogenous defect representations. The problem of overlapping defects is addressed, resulting in a second algorithm using the Radon transform coupled with data clustering.
Prior to classification salient features are extracted from a set of synthetic binary defect images to form feature vectors. The novel idea of image object classification using 100% fuzzy inference is described, and results are shown to be superior to results obtained by feature space classifiers. The sub-classification of crack defects is carried out using a heuristic classifier, and the parameterisation of pellet defects is described.