Automated Approach To Classification Of Mine-Like Objects Using Multiple-Aspect Sonar Images
نویسندگان
چکیده
منابع مشابه
Comparison of Learned versus Engineered Features for Classification of Mine Like Objects from Raw Sonar Images
Advances in high frequency sonar have provided increasing resolution of sea bottom objects, providing higher fidelity sonar data for automated target recognition tools. Here we investigate if advanced techniques in the field of visual object recognition and machine learning can be applied to classify mine-like objects from such sonar data. In particular, we investigate if the recently popular S...
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The majority of existing automatic mine detection algorithms which have been developed are robust at detecting mine-like objects (MLOs) at the expense of detecting many false alarms. These objects must later be classified as mine or not-mine. The authors present a model based technique using Dempster–Shafer information theory to extend the standard mine/not-mine classification procedure to prov...
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of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii Chapter
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Automatic Detection And Classification (ADAC) is a system to detect and classify the underwater objects for mine hunting applications. The segmentation, feature extraction, and classification are the main steps involved in the system. Two design issues in the system, the selection of the optimal classifier and the selection of the optimal feature subset. The comparison of classification systems...
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In many research areas, intelligent recognition and classification systems gained an important role. The reliability and the success of these systems are depend on the effectiveness of applied data preprocessing techniques and neural networks which can be used for efficient modeling of human’s visual system during the recognition or classification of patterns. Neural networks have an important ...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence and Soft Computing Research
سال: 2014
ISSN: 2083-2567
DOI: 10.1515/jaiscr-2015-0004