{"id":{"repo_id":"essex","oai_identifier":"oai:repository.essex.ac.uk:18293"},"canonical_url":"https://search.dev.ndltd.org/etd/essex/oai:repository.essex.ac.uk:18293","repository":{"repo_id":"essex","name":"University of Essex","base_url":"https://repository.essex.ac.uk/cgi/oai2"},"display":{"title":"An Approach to Automatic Selection of the Optimal Local Feature Detector","abstract":"Feature matching techniques have significantly contributed in making vision applications more reliable by solving the image correspondence problem. The feature matching process requires an effective feature detection stage capable of providing high quality interest points. The effort of the research community in this field has produced a wide number of different approaches to the problem of feature detection. However, imaging conditions influence the performance of a feature detector, making it suitable only for a limited range of applications. This thesis aims to improve the reliability and effectiveness of feature detection by proposing an approach for the automatic selection of the optimal feature detector in relation to the input image characteristics. Having knowledge of how the imaging conditions will influence a feature detector's performance is fundamental to this research. Thus, the behaviour of feature detectors under varying image changes and in relation to the scene content is investigated. The results obtained through analysis allowed to make the first but important step towards a fully adaptive selection method of the optimal feature detector for any given operating condition.","abstract_html":"Feature matching techniques have significantly contributed in making vision applications more reliable by solving the image correspondence problem. The feature matching process requires an effective feature detection stage capable of providing high quality interest points. The effort of the research community in this field has produced a wide number of different approaches to the problem of feature detection. However, imaging conditions influence the performance of a feature detector, making it suitable only for a limited range of applications. This thesis aims to improve the reliability and effectiveness of feature detection by proposing an approach for the automatic selection of the optimal feature detector in relation to the input image characteristics. Having knowledge of how the imaging conditions will influence a feature detector's performance is fundamental to this research. Thus, the behaviour of feature detectors under varying image changes and in relation to the scene content is investigated. 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However, imaging conditions influence the performance of a feature detector, making it suitable only for a limited range of applications. This thesis aims to improve the reliability and effectiveness of feature detection by proposing an approach for the automatic selection of the optimal feature detector in relation to the input image characteristics. Having knowledge of how the imaging conditions will influence a feature detector's performance is fundamental to this research. Thus, the behaviour of feature detectors under varying image changes and in relation to the scene content is investigated. 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However, imaging conditions influence the performance of a feature detector, making it suitable only for a limited range of applications. This thesis aims to improve the reliability and effectiveness of feature detection by proposing an approach for the automatic selection of the optimal feature detector in relation to the input image characteristics. Having knowledge of how the imaging conditions will influence a feature detector's performance is fundamental to this research. Thus, the behaviour of feature detectors under varying image changes and in relation to the scene content is investigated. 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