Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Includes bibliographical references (p. 134-138).

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 90-94). Also available in electronic version.

Thesis (Ph. D.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 140-146). Also available on the World Wide Web.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008. / Vela, Patricio, Committee Member ; Tannenbaum, Allen, Committee Member ; Yezzi, Anthony, Committee Chair ; Turk, Greg, Committee Member ; Lanterman, Aaron, Committee Member.

Thesis (M.Sc.)--York University, 2004. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 155-159). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss &rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR11828

Thesis (M.S.)--Naval Postgraduate School, 2006. / "December 2006." Includes bibliographical references (p. 47-48). Also available online from the Web site of the Naval Postgraduate School (http://www.nps.edu).

Thesis (Ph.D.)--York University, 2005. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 166-179). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNR11643

No description available.

This paper bridges the gap between variable selection methods (e.g., Pearson coefficients, KS test) and dimensionality reductionalgorithms (e.g., PCA, LDA). Variable selection algorithms encounter difficulties dealing with highly correlated data,since many features are similar in quality. Dimensionality reduction algorithms tend to combine all variables and cannotselect a subset of significant variables.Our approach combines both methodologies by applying variable selection followed by dimensionality reduction. Thiscombination makes sense only when using the same utility function in both stages, which we do. The resulting algorithmbenefits from complex features as variable selection algorithms do, and at the same time enjoys the benefits of dimensionalityreduction.1

We describe an algorithm that takes as inputs a coarse3D model of an environment, and a video sequence acquiredwithin the environment, and produces as output an estimateof the camera’s 6-DOF egomotion expressed in the coordinatesof the 3D model. Our method has several novelaspects: it performs line-based structure-from-motion; italigns the local line constellation to the known model; andit uses off-line visibility analysis to dramatically acceleratethe alignment process.We present simulation results demonstrating themethod’s operation in a multi-room environment. We showthat the method can estimate metric egomotion accuratelyand could be used for for many minutes of operation andthousands of video frames.