Keung Chi-Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 128-135). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Classification --- p.2 / Chapter 1.2 --- Instance-Based Learning --- p.4 / Chapter 1.2.1 --- Three Basic Components --- p.5 / Chapter 1.2.2 --- Advantages --- p.6 / Chapter 1.2.3 --- Disadvantages --- p.7 / Chapter 1.3 --- Thesis Contributions --- p.7 / Chapter 1.4 --- Thesis Organization --- p.8 / Chapter 2 --- Background --- p.10 / Chapter 2.1 --- Improving Instance-Based Learning --- p.10 / Chapter 2.1.1 --- Scaling-up Nearest Neighbor Searching --- p.11 / Chapter 2.1.2 --- Data Reduction --- p.12 / Chapter 2.2 --- Prototype Learning --- p.12 / Chapter 2.2.1 --- Objectives --- p.13 / Chapter 2.2.2 --- Two Types of Prototype Learning --- p.15 / Chapter 2.3 --- Instance-Filtering Methods --- p.15 / Chapter 2.3.1 --- Retaining Border Instances --- p.16 / Chapter 2.3.2 --- Removing Border Instances --- p.21 / Chapter 2.3.3 --- Retaining Center Instances --- p.22 / Chapter 2.3.4 --- Advantages --- p.23 / Chapter 2.3.5 --- Disadvantages --- p.24 / Chapter 2.4 --- Instance-Abstraction Methods --- p.25 / Chapter 2.4.1 --- Advantages --- p.30 / Chapter 2.4.2 --- Disadvantages --- p.30 / Chapter 2.5 --- Other Methods --- p.32 / Chapter 2.6 --- Summary --- p.34 / Chapter 3 --- Integration of Filtering and Abstraction --- p.36 / Chapter 3.1 --- Incremental Integration --- p.37 / Chapter 3.1.1 --- Motivation --- p.37 / Chapter 3.1.2 --- The Integration Method --- p.40 / Chapter 3.1.3 --- Issues --- p.41 / Chapter 3.2 --- Concept Integration --- p.42 / Chapter 3.2.1 --- Motivation --- p.43 / Chapter 3.2.2 --- The Integration Method --- p.44 / Chapter 3.2.3 --- Issues --- p.45 / Chapter 3.3 --- Difference between Integration Methods and Composite Clas- sifiers --- p.48 / Chapter 4 --- The PGF Framework --- p.49 / Chapter 4.1 --- The PGF1 Algorithm --- p.50 / Chapter 4.1.1 --- Instance-Filtering Component --- p.51 / Chapter 4.1.2 --- Instance-Abstraction Component --- p.52 / Chapter 4.2 --- The PGF2 Algorithm --- p.56 / Chapter 4.3 --- Empirical Analysis --- p.57 / Chapter 4.3.1 --- Experimental Setup --- p.57 / Chapter 4.3.2 --- Results of PGF Algorithms --- p.59 / Chapter 4.3.3 --- Analysis of PGF1 --- p.61 / Chapter 4.3.4 --- Analysis of PGF2 --- p.63 / Chapter 4.3.5 --- Overall Behavior of PGF --- p.66 / Chapter 4.3.6 --- Comparisons with Other Approaches --- p.69 / Chapter 4.4 --- Time Complexity --- p.72 / Chapter 4.4.1 --- Filtering Components --- p.72 / Chapter 4.4.2 --- Abstraction Component --- p.74 / Chapter 4.4.3 --- PGF Algorithms --- p.74 / Chapter 4.5 --- Summary --- p.75 / Chapter 5 --- Integrated Concept Prototype Learner --- p.77 / Chapter 5.1 --- Motivation --- p.78 / Chapter 5.2 --- Abstraction Component --- p.80 / Chapter 5.2.1 --- Issues for Abstraction --- p.80 / Chapter 5.2.2 --- Investigation on Typicality --- p.82 / Chapter 5.2.3 --- Typicality in Abstraction --- p.85 / Chapter 5.2.4 --- The TPA algorithm --- p.86 / Chapter 5.2.5 --- Analysis of TPA --- p.90 / Chapter 5.3 --- Filtering Component --- p.93 / Chapter 5.3.1 --- Investigation on Associate --- p.96 / Chapter 5.3.2 --- The RT2 Algorithm --- p.100 / Chapter 5.3.3 --- Analysis of RT2 --- p.101 / Chapter 5.4 --- Concept Integration --- p.103 / Chapter 5.4.1 --- The ICPL Algorithm --- p.104 / Chapter 5.4.2 --- Analysis of ICPL --- p.106 / Chapter 5.5 --- Empirical Analysis --- p.106 / Chapter 5.5.1 --- Experimental Setup --- p.106 / Chapter 5.5.2 --- Results of ICPL Algorithm --- p.109 / Chapter 5.5.3 --- Comparisons with Pure Abstraction and Pure Filtering --- p.110 / Chapter 5.5.4 --- Comparisons with Other Approaches --- p.114 / Chapter 5.6 --- Time Complexity --- p.119 / Chapter 5.7 --- Summary --- p.120 / Chapter 6 --- Conclusions and Future Work --- p.122 / Chapter 6.1 --- Conclusions --- p.122 / Chapter 6.2 --- Future Work --- p.126 / Bibliography --- p.128 / Chapter A --- Detailed Information for Tested Data Sets --- p.136 / Chapter B --- Detailed Experimental Results for PGF --- p.138

This thesis involves the development of a three-dimensional measurement system for digitising the surface of an object. The measurement system consists of a single camera and a four point planar target of known size. The target is hand held, and is used to probe the surface of the object being measured. The position of the target is tracked by the camera, and the contact point on the object is determined. The vision based digitising technique can be used in the industrial and engineering design fields during the product development phase. The accuracy of measurement is an important criterion for establishing the success of the 3-D measurement system, and the factors influencing the accuracy are investigated. These factors include the image processing algorithm, the intrinsic parameters of the camera, the algorithm to determine the position, and various procedural variables. A new iterative algorithm is developed to calculate position. This algorithm is evaluated, and its performance is compared to that of an analytic algorithm. Simple calibration procedures are developed to determine the intrinsic parameters, and mathematical models are constructed to justify these procedures. The performance of the 3-D measurement system is established and compared to that of existing digitising systems.

Thesis (M.Sc.)--York University, 2003. Graduate Programme in Mathematics and Statistics. / Typescript. Includes bibliographical references (leaves 112-121) and index. 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%5Fval%5Ffmt=info:ofi/fmt:kev:mtx:dissertation&rft%5Fdat=xri:pqdiss:MQ99310

Thesis (M. Sc.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on Nov. 27, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta." Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 96-99).

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 107-114). Available also in a digital version from Dissertation Abstracts.

Includes bibliographical references (p. 85-86).

Thesis (Ph. D.)--Oregon State University, 2002. / Typescript (photocopy). Includes bibliographical references (leaves 168-171). Also available on the World Wide Web.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 71-72).

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Supervisor: Earl E. Swartzlander, Jr. Vita. Includes bibliographical references. Also available from UMI.