Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/68532
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dc.contributor.authorVu Hoang Minh-
dc.date.accessioned2016-05-26T07:51:32Z-
dc.date.available2016-05-26T07:51:32Z-
dc.date.issued2016-
dc.identifier.urihttp://hdl.handle.net/10356/68532-
dc.description.abstractIn this thesis, popular techniques within the area of machine vision: noise reduction, feature detection, edge detection and feature tracking, have been studied. This project is concerned with the use of thermal-infrared cameras which are much less affected by changes in lighting, shadows and out-of-view motion compared to visible cameras. The main research focus of this thesis is how to deal with the low signal-to-noise ratio of thermal-infrared video in developing a novel real-time methodology for robust feature detection and tracking. The thesis first reviews the background of thermal-infrared imagery. It then covers the necessity of a noise reduction filter in thermal-infrared video. Next, it presents a number of existing approaches in edge and feature detection followed by four proposed techniques. Finally, results reveal that the proposed techniques perform well in thermal-infrared video.en_US
dc.format.extent129 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleRobust feature detection and tracking in thermal-infrared videoen_US
dc.typeThesis
dc.contributor.supervisorCheah Chien Chernen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
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