Önder, Onur2016-01-082016-01-082007http://hdl.handle.net/11693/14548Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2007.Thesis (Master's) -- Bilkent University, 2007.Includes bibliographical references leaves 36-38Two new methods for combined filtering and key-frame reduction of motion capture data are proposed. Filtering of motion capture data is necessary to eliminate any jitter introduced by a motion capture system. Although jitter removal is needed to obtain a more realistic animation, it may result in an oversmoothed motion data if it is not done properly. Key-frame reduction, on the other hand, allows animators to easily edit motion data by representing animation curves with a significantly smaller number of key frames. One of the proposed techniques achieves key frame reduction and jitter removal simultaneously by fitting a Hermite curve to motion capture data using dynamic programming. Another method is to use curve simplification algorithms on the motion capture data until the desired reduction is reached. In this research, the results of these algorithms are evaluated and compared. Both subjective and objective results are presented.xi, 62 leaves, illustrations, tablesEnglishinfo:eu-repo/semantics/openAccessMotion CaptureKeyframe ReductionCurve FittingCurve Simpli-ficationNoise FilteringQA76.754 .O53 2007Framework (Computer programs)Computer graphics.Curve fitting.ThesisBILKUTUPB103843