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 ...