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Adaptive model-based motion estimation

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dc.contributor.advisor Kolodziej, Wojciech J.
dc.creator Crinon, Regis
dc.date.accessioned 2010-12-10T16:32:34Z
dc.date.available 2010-12-10T16:32:34Z
dc.date.copyright 1993-05-27
dc.date.issued 1993-05-27
dc.identifier.uri http://hdl.handle.net/1957/19502
dc.description Graduation date: 1994 en_US
dc.description.abstract A general discrete-time, adaptive, multidimensional framework is introduced for estimating the motion of one or several object features from their successive non-linear projections on an image plane. The motion model consists of a set of linear difference equations with parameters estimated recursively from a non-linear observation equation. The model dimensionality corresponds to that of the original, non-projected motion space thus allowing to compensate for variable projection characteristics such as panning and zooming of the camera. Extended recursive least-squares and linear-quadratic tracking algorithms are used to adaptively adjust the model parameters and minimize the errors of either smoothing, filtering or predicting the object trajectories in the projection plane. Both algorithms are derived using a second order approximation of the projection nonlinearities. All the results presented here use a generalized vectorial notation suitable for motion estimation of any finite number of object features and various approximations of the nonlinear projection. An example of motion estimation in a sequence of video frames is given to illustrate the main concepts. en_US
dc.language.iso en_US en_US
dc.subject.lcsh Computer vision -- Mathematical models en_US
dc.title Adaptive model-based motion estimation en_US
dc.type Thesis/Dissertation en_US
dc.degree.name Doctor of Philosophy (Ph. D.) in Electrical and Computer Engineering en_US
dc.degree.level Doctoral en_US
dc.degree.discipline Engineering en_US
dc.degree.grantor Oregon State University en_US
dc.description.digitization File scanned at 300 ppi (Moochrome, 256 Grayscale) using Capture Perfect 3.0.82 on a Canon DR-9080C in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR. en_US

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