**General Links**

- Computer Vision Home Page
- Web Resources in Computer Vision
- Computer Vision Handbook
- Sussex Computer Vision Course
- Another on-line Computer Vision Course
- Computer Graphics Home Page
- Image Manipulation and Storage
- Interactive Imaging Machines

- Pattern Recognition Course on the Web (by Richard O. Duda)
- Classification Society of North America
- Pattern Recognition Information
- Nerual Network FAQ's
- Machine Learning Resources
- Neural Network Information
- Face Recognition Home Page
- Handwriting Recognition
- Java Demos for Handwriting Recognition

**Specific Links**

- Introduction to pattern recognition (PostScript file)
- Transducers (digital cameras and CCD document scanners)
- Digital images (pixels, bit depth and color)
- Lots more about imaging and images
- Image processing basics
- Stretching and histogram equalization
- Optical character recognition (brief introduction)
- Handwritten address recognition demonstration
- Grids, connectivity and contour tracing (PostScript file)
- M.I.T. reading machine for the blind
- What is hysteresis?
- Hysteresis smoothing (digital filtering)

- Regularization
- Logical smoothing
- Local averaging
- Median filtering
- Gaussian smoothing
- Polygonal approximation
- Midpoint smoothing
- Ramer-Douglas-Peucker algorithm
- Interactive Java applets by Steve Robbins
- Smoothing basics (PostScript file)
- Function approximation

- Edge detection and the Sobel operator
- Canny edge-detector demo
- Mach bands and lateral inhibition
- Limulus-the horseshoe crab
- Sharpening, the Laplacian and lateral inhibition in neural networks (PostScript file)
- Laplacian edges
- Unsharp masking
- More edge detection

- Moments of univariate distributions, skew and kurtosis (PostScript file)
- Basics on multivariate moments (PostScript file)
- Moments of area & perimeter
- Moments for feature extraction
- Moments for pre-processing
- Moments as predictors of discrimination performance

- Distance between sets
- Medial Axis (prairie-fire transformation)
- Skeletons (PostScript file)
- Hilditch's algorithm
- Rosenfeld's algorithm
- Skeletonization software
- Minkowski metrics
- Distance transforms
- Skeleton clean-up via distance transforms
- Medial axes via distance transforms
- Medial axis transform
- Medial axis in 3D with applications

- Convex hulls, concavities and enclosures
- Interactive Java convex hull algorithms in 2D
- Clarkson's code for 2D convex hulls

- Basics of Chain Coding (PostScript file)
- Square, circular, and grid-intersect quantization
- Probability of obtaining diagonal elements
- Geometric probability
- Bertrand's paradox (with Java applet simulations)
- Difference encoding & chain correlation functions
- Minkowski metric quantization

- Point-to-curve transformations (Hough transform)
- Hough Transform tutorial
- Line and circle detection
- Hypothesis testing approach
- Maximum-entropy quantization
- Hough Transform demo on satellite photos
- Hough Transform home page (and software)
- Hough Transform publications
- More Hough Transform code
- Interactive histogram with Java applet
- Proximity graphs and perception
- Minimal spanning tree (MST) of a dot pattern
- MST interactive Java applet
- Delaunay Triangulations and Voronoi diagrams
- The shape of a set of points
- Relative neighbourhood graphs
- Sphere-of-influence graphs
- Alpha shapes
- Beta skeletons

- Neural Network Basics (FAQ's)
- Neural Network Basics (with Java)
- Formal neurons, linear machines & perceptrons
- Introduction to Probability and Statistics
- Basics of Statistical Pattern Recognition
- Minimum risk classification
- Minimum error classification
- Discriminant functions (linear, quadratic, polynomial)
- The multivariate Gaussian probability density function
- Mahalanobis distance classifiers
- Parametric decision rules
- Learning from examples

- Independence in the discrete case
- Conditional and unconditional independence
- Dependence and correlation
- The best k measurements are not the k best
- Information theory and feature evaluation criteria
- Feature selection methods
- Models of spatial dependence between features

- General Learning Resources
- Perceptrons
- Non-parametric training of linear machines
- Error-correction procedures
- Rosenblatt's Learning Algorithm (an interactive Java applet)
- The fundamental learning theorem
- Multi-layer networks
- Reinforcement Learning - An Intercative Tutorial

- Properties of estimators
- Dimensionality and sample size
- Estimation of the probability of misclassification

- The k-nearest neighbor rule
- Efficient search methods for nearest neighbors
- Decreasing space requirements
- Editing training sets (compressed PostScript file)
- Error bounds
- Nearest neighbor computation software

- Markov methods
- Forward dynamic programming and The Viterbi algorithm
- Combined bottom-up and top-down algorithms

- Decision-directed learning (the K-means algorithm)
- Graph-theoretic methods
- Agglomerative and divisive methods
- Clustering software on the Web