A knowledge-based machine vision system
| dc.contributor.author | Lu, Ning | |
| dc.date.accessioned | 2020-02-26T09:15:04Z | |
| dc.date.available | 2020-02-26T09:15:04Z | |
| dc.date.issued | 1997 | |
| dc.description | A thesis submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the degree of Doctor of Philosophy | en_ZA |
| dc.description.abstract | As a result of the work on this project, two machine vision systems are presented in this thesis. The first system is designed for an industrial application which identifies and locates machined components which have general geometric boundaries. The system consists of a CCD camera, a Pi030 image processing system, and a host computer running under the Linux operating system. The problem of extracting the shape information is treated as a two level image processing procedure, viz. Segmentation and decomposition. Objects are first segmented into generic ‘blobs’ and each blob is further decomposed into a primitive chain which constitutes a series of dominant points and basic primitive segments. The recognition task is accomplished by applying the knowledge base techniques. The shape information is used to form an object description using a specific scheme which is a combination of a semantic network, feature vectors and a production system. The knowledge base is constructed with this description scheme. The inference engine is based on an object verification procedure and a blob verification procedure. The inference results are also used as feedback information to improve the image processing procedure. Experimental results have proved that this system has the advantages of accuracy and reliability. In tests conducted on 19 machined components with different combinations of generic features, the classification system showed a 100% success rate. In addition the system is capable of being easily modified and expanded to cater for parts that are touching and/or overlapping as well as parts in motion on a conveyor. The second machine vision system has been separately designed to carry out the fruit inspection and classification tasks automatically. The system uses a low cost Personal Computer (PC) and an image acquisition hardware to perform these tasks. The concept of fuzzy sets is employed to design the fruit classifier. Preliminary results have proved the feasibility of the system in which is felt to be suitable for individual farmers who wish to carry out automatic fruit classification. | en_ZA |
| dc.description.librarian | Andrew Chakane 2020 | en_ZA |
| dc.identifier.uri | https://hdl.handle.net/10539/28954 | |
| dc.language.iso | en | en_ZA |
| dc.subject | Computer vision. | en_ZA |
| dc.subject | Artifical intelligence. | en_ZA |
| dc.subject | Image processing. | en_ZA |
| dc.subject | Pattern recognition systems. | en_ZA |
| dc.subject | Robot visions. | en_ZA |
| dc.title | A knowledge-based machine vision system | en_ZA |
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