Cüce, Halil İbrahim2016-07-012016-07-012005http://hdl.handle.net/11693/29667Cataloged from PDF version of article.In this thesis, image and video analysis algorithms are developed. Tracking moving objects in video have important applications ranging from CCTV (Closed Circuit Television Systems) to infrared cameras. In current CCTV systems, 80% of the time, it is impossible to recognize suspects from the recorded scenes. Therefore, it is very important to get a close shot of a person so that his or her face is recognizable. To take high-resolution pictures of moving objects, a pan-tiltzoom camera should automatically follow moving objects and record them. In this thesis, a mean-shift based moving object tracking algorithm is developed. In ordinary mean-shift tracking algorithm a color histogram or a probability density function (pdf) estimated from image pixels is used to represent the moving object. In our case, a joint-probability density function is used to represent the object. The joint-pdf is estimated from the object pixels and their wavelet transform coefficients. In this way, relations between neighboring pixels, edge and texture information of the moving object are also represented because wavelet coefficients are obtained after high-pass filtering. Due to this reason the new tracking algorithm is more robust than ordinary mean-shift tracking using only color information. A new content based image retrieval (CBIR) system is also developed in this thesis. The CBIR system is based on mean-shift analysis using a joint-pdf. In this system, the user selects a window in an image or an entire image and queries similar images stored in a database. The selected region is represented using a joint-pdf estimated from image pixels and their wavelet transform coefficients. The retrieval algorithm is more reliable compared to other CBIR systems using only color information or only edge or texture information because the jointpdf based approach represents both texture, edge and color information. The proposed method is also computationally efficient compared to sliding-window based retrieval systems because the joint-pdfs are compared in non-overlapping windows. Whenever there is a reasonable amount of match between the queried window and the original image window then a mean-shift analysis is started.xiii, 61 leaves, illustrations, graphicsEnglishinfo:eu-repo/semantics/openAccessMean-shift trackingFLIR imagerySaliency detectionColor histogramContent-based image retrievalQA76.575 .C83 2005Multimedia systems.ThesisBILKUTUPB091935