Dong tai yi zhuan yan shuo fa zhi zi you xing tai mian pin qi yu mo xing jie he de ying yong

Chow Chi Kin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 136-139).Text in English; abstracts in English and Chinese.Chow Chi Kin.Abstract --- p.i摘要 --- p.iiAcknowledgement --- p.iiiTable of content --- p.ivList of figures --- p.viiiChapter Chapter 1: --- Introduction --- p.1Chapter Chapter 2: --- What is Surface Registration? --- p.7Chapter 2.1 --- What is free-form surface --- p.7Chapter 2.2 --- Methodologies of surface construction --- p.8Chapter 2.2.1 --- CAD Model --- p.9Chapter 2.2.2 --- 3D Reconstruction algorithm --- p.11Chapter 2.2.3 --- Geometric/Range sensors --- p.12Chapter 2.3 --- What is Surface Registration --- p.14Chapter 2.4 --- Purpose of Surface Registration --- p.19Chapter 2.5 --- Review of exist registration algorithms --- p.20Chapter 2.5.1 --- ICP (Iterative Closest Point) algorithm --- p.20Chapter A. --- Overview of ICP --- p.21Chapter B. --- ICP Kernel --- p.22Chapter C. --- Possible pre-processings of ICP --- p.23Chapter D. --- Multiple features in the closest point search --- p.24Chapter E. --- Advantages of ICP --- p.25Chapter F --- . Disadvantages of ICP --- p.26Chapter 2.5.2 --- "Feature-based, registration" --- p.27Chapter 2.5.3 --- Genetic algorithm approach --- p.28Chapter Chapter 3: --- What is Genetic Algorithm? --- p.32Chapter 3.1 --- What is Genetic Algorithm (GA) --- p.32Chapter 3.2 --- Exists Search Methods --- p.33Chapter 3.3 --- Mechanism of Genetic Algorithms --- p.39Chapter 3.3.1 --- Initialization --- p.41Chapter 3.3.2 --- Reproduction --- p.41Chapter A. --- Cross-Over --- p.42Chapter B. --- Mutation --- p.44Chapter 3.3.3 --- Selection --- p.47Chapter A. --- Best Fitness Selection --- p.47Chapter B. --- Probabilistic Selection --- p.49Chapter 3.3.4 --- Termination checking --- p.52Chapter A. --- Fixed number of generation --- p.53Chapter B. --- Adaptive number of generation --- p.53Chapter 3.3.5 --- Solution Space --- p.54Chapter 3.3.6 --- Formation of chromosome --- p.54Chapter 3.3.7 --- Fitness function --- p.55Chapter 3.4 --- Examples of function optimization using Genetic Algorithm --- p.55Experiment 1: 2D Sphere function --- p.56Experiment 2: 2D Sinc function --- p.58Experiment 3: Foxholes function --- p.61Experiment 4: Steps Exponential Decay function --- p.63Chapter 3.5 --- Convergence Theorem of Genetic Algorithm --- p.66Chapter Chapter 4: --- Surface Registration as an Optimization --- p.71Chapter 4.1 --- Solution space --- p.75Chapter 4.2 --- Formation of Gene and Chromosome --- p.79Chapter 4.3 --- Fitness Function --- p.83Chapter 4.4 --- Genetic Algorithm VS. Steepest Gradient Descent --- p.88Chapter 4.5 --- Reproduction --- p.94Chapter 4.5.1 --- Cross-Over --- p.94Chapter 4.5.2 --- Mutation --- p.95Chapter 4.5.3 --- Variant --- p.95Chapter 4.6 --- Selection --- p.101Chapter 4.7 --- Dynamic Boundary --- p.103Chapter 4.8 --- Nearest Neighbor Search --- p.108Chapter Chapter 5: --- Experimental Results --- p.112Chapter 5.1 --- Surface Registration --- p.113Chapter 5.2 --- Model Integration --- p.114Chapter A. --- Human skull --- p.115Chapter B. --- Easter Island stone --- p.115Chapter C. --- Child --- p.116Chapter D. --- Dinosaur head --- p.116Chapter 5.3 --- Noise Sensitivity --- p.117Chapter 5.3.1 --- Addition of Gaussian noise --- p.117Chapter A. --- Human Skull --- p.117Chapter B. --- Human foot bone --- p.119Chapter C. --- Human heart --- p.120Chapter D. --- Human vertebrae --- p.122Chapter E. --- Human fetus --- p.125Chapter 5.3.2 --- Smoothing of pre-added Gaussian noise --- p.127Chapter A. --- Human skull --- p.128Chapter B. --- Human foot bone --- p.129Chapter 5.4 --- Model Integration of Real Images --- p.130Chapter Chapter 6: --- Conclusion --- p.133References --- p.13