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Contents
Vision Through Optimization
N.A.Thacker and T.F.Cootes
Contents
Statistical Background
Introduction
Basic Definitions of Notation
Bayes Theorem
Maximum Likelihood
Least Squares Approaches
Lagrange Techniques
Model Selecton
Parameter Estimation
Covariance Estimation
Optimal Combination
Error Propagation
Theory: Optimization Methods
Introduction
Minimization in One Dimension
Golden Section Search
Multi-Dimensional Search
Multi-Dimensional Search (No derivatives)
Conjugate Directions
Multi-Dimensional Search (With First Derivatives)
Quasi-Newton (Variable Metric) Methods
Use of Second Derivatives : Levenberg-Marquardt
Memory Constraints
Global Optimization
Simplex Algorithm
Simulated Annealing
Genetic Algorithms
Multi-Resolution Methods and Graduated Non-Convexity
The Hough Transform
Applications
Curve Fitting
Fitting Straight Lines
Fitting General Curves
Fitting Parameterized Models to Image Data
Grey model fitting
Active Shape Model Location
Stereo Camera Calibration
Hough Transform SEM Calibration
References
About this document ...
Bob Fisher
Fri Mar 28 14:12:50 GMT 1997