Enhanced And Automated Approaches For Fish Recognition And Classification System
| dc.contributor.author | Samma, Ali Salem Ali | |
| dc.date.accessioned | 2019-09-03T04:14:10Z | |
| dc.date.available | 2019-09-03T04:14:10Z | |
| dc.date.issued | 2011-06 | |
| dc.description.abstract | Recognition and classification of fish images with high degree of accuracy and efficiency can be a difficult task due to fish being very similar to the background, missing of some features and high cost of computation. The aim of this thesis is to overcome these problems by proposing methods that overcome the problem of missing features of fish. The problems of high cost of computation, inaccurate extraction and representation of features of fish, and inaccurate and inefficient selection of desirable shape for fish recognition and classification are also addressed. Furthermore, for automated detection and extraction of the fish, K-means and background subtraction approaches for image segmentation are enhanced. An enhanced approach for shape representation that combines run-length method and modified chain code method with region information is also proposed. An enhanced approach for shape description that uses slope is also proposed to reduce the computation time. For more accurate and efficient detection of the critical points for a shape, a technique that combines skeleton and boundary information is proposed. Finally, for a more accurate classification of fish, methods that use principal component analysis and genetic algorithm with two methods of the support vector machine are proposed. By utilising the above-mention enhanced approaches, an automated fish recognition and classification system is also established. The enhanced methods make the fish recognition and classification system achieves 98.4% accuracy and more accurate than the existing systems as shown by the evaluation that has been carried out. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/8776 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.subject | Fish Recognition | en_US |
| dc.subject | Classification System | en_US |
| dc.title | Enhanced And Automated Approaches For Fish Recognition And Classification System | en_US |
| dc.type | Thesis | en_US |
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