Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2004. / Includes bibliographical references (leaves 86-88). Also available in electronic version. Access restricted to campus users.

Thesis (M.S.)--Rochester Institute of Technology, 1994. / Typescript. Includes bibliographical references (leaves [125-126]).

Thesis (Ph. D.)--Computing, Georgia Institute of Technology, 2007. / Aaron Bobick, Committee Chair ; Allen Tannenbaum, Committee Co-Chair ; Greg Turk, Committee Member ; Steven Haker, Committee Member ; W. Eric. L. Grimson, Committee Member.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (Ph.D.)--University of Delaware, 2005. / Principal faculty advisor: Chandra Kambhamettu, Dept. of Computer and Information Sciences. Includes bibliographical references.

Hand detection, segmentation, and pose recognition are challenging problems in Computer Vision with a wide variety of potential applications like alternative input devices, surveillance, motion capture, and augmented reality. This work proposes methods to solve each of these problems in high-resolution, monochromatic images via shape and texture-based methods. Hand Detection and Segmentation: This method of hand detection is based upon the outputs from both line-finding and curve-finding algorithms to find shapes that appear to be finger-like. A series of tests is performed on each finger candidate to further remove false positives and determine which sets of them could possibly form a human hand. Pose Recognition: Pose recognition works on database model, taking as input both a test image and a database of all possible hand poses. By using a scoring system comprised of votes between two different distance measures, the algorithm returns a list of database images in order of similarity to the test image. Pose Detection in a Video: To determine if a given hand pose occurs in the frames of a video sequence, the algorithm performs the pose recognition method described above, but inputs the pose to look for as the test image and the video sequence as the "database." It then returns a list of frames in the sequence in order of similarity to the given pose.

Thesis (Ph.D.)--University of Wollongong, 2005. / Typescript. Includes bibliographical references: p. 333-339.

Thesis (M.S.)--State University of New York at Binghamton, Department of Computer Science, Thomas J. Watson School of Engineering and Applied Science, 2007. / Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 1995. / Typescript. Bibliography: leaves 110-111.

Thesis (M.Sc.)--York University, 2005. Graduate Programme in Physics and Astronomy. / Typescript. Includes bibliographical references (leaves 167-174). 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:MR11763