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dc.contributor.advisorÇetin, A. Enisen_US
dc.contributor.authorÇoğun, Fuaten_US
dc.date.accessioned2016-01-08T18:14:00Z
dc.date.available2016-01-08T18:14:00Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/11693/15137
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2010.en_US
dc.descriptionIncludes bibliographical references leaves 43-46.en_US
dc.description.abstractMoving shadows constitute problems in various applications such as image segmentation, smoke detection and object tracking. Main cause of these problems is the misclassification of the shadow pixels as target pixels. Therefore, the use of an accurate and reliable shadow detection method is essential to realize intelligent video processing applications. In the first part of the thesis, a cepstrum based method for moving shadow detection is presented. The proposed method is tested on outdoor and indoor video sequences using well-known benchmark test sets. To show the improvements over previous approaches, quantitative metrics are introduced and comparisons based on these metrics are made. Most video processing applications require object tracking as it is the base operation for real-time implementations such as surveillance, monitoring and video compression. Therefore, accurate tracking of an object under varying scene and illumination conditions is crucial for robustness. It is well known that illumination variations on the observed scene and target are an obstacle against robust object tracking causing the tracker lose the target. In the second part of the thesis, a two dimensional (2D) cepstrum based approach is proposed to overcome this problem. Cepstral domain features extracted from the target region are introduced into the covariance tracking algorithm and it is experimentally observed that 2D-cepstrum analysis of the target region provides robustness to varying illumination conditions. Another contribution is the development of the co-difference matrix based object tracking instead of the recently introduced covariance matrix based method. One of the problems with most target tracking methods is that they do not have a well-established control mechanism for target loss which usually occur when illumination conditions suddenly change. In the final part of the thesis, a confidence interval based statistical method is developed for target loss detection. Upper and lower bound functions on the cumulative density function (cdf) of the target feature vector are estimated for a given confidence level. Whenever the estimated cdf of the detected region exceeds the bounds it means that the target is no longer tracked by the tracking algorithm. The method is applicable to most tracking algorithms using features of the target image region.en_US
dc.description.statementofresponsibilityÇoğun, Fuaten_US
dc.format.extentx, 46 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMoving Shadow Detectionen_US
dc.subject2D-Cepstrum Analysisen_US
dc.subjectObject Tracking under Illumination Conditionsen_US
dc.subjectTarget Loss Detectionen_US
dc.subject.lccTA1637 .C64 2010en_US
dc.subject.lcshImage processing--Digital techniques.en_US
dc.subject.lcshDigital video.en_US
dc.subject.lcshVideo compression.en_US
dc.subject.lcshControl theory.en_US
dc.titleVideo processing methods robust to illumination variationsen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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