published_or_final_version / Electrical Engineering / Master / Master of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

A topic of computer vision that has been recently studied by a substantial number of scientists is the recognition of objects in digitized gray scale images. The primary goal of model-based object recognition research is the efficient and precise matching of features extracted from sensory data with the corresponding features in an object model database. A source of difficulty during the feature extraction is the determination and representation of pertinent attributes from the sensory data of the objects in the image. In addition, features which are visible from a single vantage point are not usually adequate for the unique identification of an object and its orientation. This paper will describe a regimen that can be used to address these problems. Image preprocessing such as edge detection, image thinning, thresholding, etc., will first be addressed. This will be followed by an in depth discussion that will center upon the extraction of local geometric feature vectors and the hypothesis-verification model used for two dimensional object recognition. / Department of Computer Science

In this study, fluorescein angiograms of the ocular fundus have been digitised to enable them to be processed and analysed by computer. A fully automated technique for counting microaneurysms (MA) in these images was developed with a view to producing an objective, accurate and highly repeatable way of quantifying these lesions. Prior to any other image processing, a number of pre-processing stages were applied in order to compensate for non-uniformaties and to remove the background fluorescence component present in all the images. Matched filters modelled on two-dimensional Gaussian distributions were employed to detect MA in the 'shade-corrected' images. A binary image representation of the vascular network was constructed. This 'vessel mask', used in conjunction with the original match-filtered images, enabled MA to be detected by grey-level thresholding the filtered images. The resulting binary objects could then be counted by the computer as MA. The automated technique was assessed by comparing the computer's results for six fluorescein angiograms with MA counts obtained by ophthalmologists analysing both analogue and digital images. The performance of both man and machine were judged with respect to 'gold standards' compiled from prints of the original negatives. The best results were obtained by the clinicians analysing the analogue prints, although they differed greatly in their ability to detect microaneurysms. The computer performed better than the clinicians when they were counting MA in the digital images and produced highly repeatable results. To improve the performance of the automated technique, images were captured at approximately four times the previous spatial resolution and a smaller area of each image was analysed. Additionally, more complex image-processing techniques were employed to increase the accuracy of the computer analysis. Although the performance of the automated technique was improved, the computer results only matched those of the clinicians' analogue analyses for two of the images.