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A range data acquisition system using a uniquely encoded light patternYee, Soung Ryong 05 1900 (has links)
No description available.
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Recognition of clutter in weather radars using polarization diversity information and artificial neural networksDa Silveira, Reinaldo Bomfim January 1999 (has links)
No description available.
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Automatic labeling, modeling and recognition for line-drawing interpretationChêng, Tsê January 1994 (has links)
Thesis (Ph. D.)--University of Hawaii at Manoa, 1994. / Includes bibliographical references (leaves 181-191). / Microfiche. / xvi, 191 leaves, bound ill. 29 cm
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Pattern recognition is a clinical reasoning process in musculoskeletal physiotherapyMiller, Peter January 2009 (has links)
Masters Research - Master of Medical Science / Pattern recognition is a non-analytical clinical reasoning process which has been reported in the medical and allied health literature for some time. At a time when clinical problem solving was largely considered to consist of the analytical process of hypothetico-deductive reasoning, pattern recognition was introduced in the literature with observations of greater efficiency and accuracy. The research that followed these apparent opposing models of clinical reasoning resulted in significant growth in the understanding of problem solving in healthcare. On commencing this thesis the knowledge surrounding pattern recognition in physiotherapy was insufficient for its inclusion in educational design. Consequently the aims of the study described in this thesis were to clearly identify pattern recognition using high fidelity case methods and observe its relationship with accuracy and efficiency. The study utilised a single case study with multiple participants. A real clinical case with a diagnosis of high grade lumbar spine spondylolisthesis was simulated using a trained actor. This provided a high fidelity case study method allowing the observation of more realistic problem solving practices as compared with the common low fidelity paper case approach. Two participant groups were included in the study to investigate the common belief that pattern recognition is an experience based reasoning process. The expert group comprised ten titled musculoskeletal physiotherapists with a minimum of ten years overall clinical experience and greater than two years experience following the completion of postgraduate study. The novice group included nine physiotherapists in their first year of clinical practice following completion of an undergraduate degree. Qualitative data collection methods included observation of the participant taking a patient history of the simulated client and a stimulated retrospective recall interview with the participant. The mixed method analysis used in the study provided methodological triangulation of the results and supported the presence of pattern recognition in musculoskeletal physiotherapy. The quantitative research findings indicated that pattern recognition was significantly more likely to produce an accurate diagnostic outcome than analytical reasoning strategies during a physiotherapy history. However its use was not a guarantee of success with only three of the four experts using pattern recognition identifying the correct diagnosis. Although four experts utilised pattern recognition as compared with only one novice, no significant overall differences were found in the use of pattern recognition between the expert and novice participant groups. The findings relating to time data found that expert participants took longer to conduct the client history than novices. Similarly those participants identified using pattern recognition also required more time which seemingly contradicts the view of pattern recognition being an efficient clinical reasoning process. This finding was limited by the incomplete nature of the study which did not include a physical examination or any client management.
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Handwritten signature verification using complementary statistical models /McCabe, Alan. January 2003 (has links)
Thesis (Ph.D.) - James Cook University, 2003. / Typescript (photocopy) Bibliography: leaves 181-196.
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Autonomous tactile object exploration and estimation using simple sensors /Hollinger, James G., January 1994 (has links)
Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 76-80). Also available via the Internet.
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Object recognition and pose estimation using appearance-based features and relational indexing /Costa, Mauro Sergio Figueiredo. January 1997 (has links)
Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (leaves [117]-123).
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Feature-based exploitation of multidimensional radar signaturesRaynal, Ann Marie, January 1900 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.
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Semantic representation and recognition of human activitiesRyoo, Michael Sahngwon, January 1900 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.
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Dynamic scene interpretation and understanding from two viewsThakoor, Ninad Shashikant. January 2009 (has links)
Thesis (Ph.D.)--University of Texas at Arlington, 2009.
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