Pattern Recognition on the Web

General Links

Computer Vision:

Pattern Recognition:

Specific Links

Introduction to Pattern Recognition via Character Recognition

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

Spatial Smoothing

  1. Regularization
  2. Logical smoothing
  3. Local averaging
  4. Median filtering
  5. Gaussian smoothing
  6. Polygonal approximation
  7. Smoothing basics (PostScript file)
  8. Function approximation

Spatial Differentiation

  1. Edge detection and the Sobel operator
  2. Canny edge-detector demo
  3. Mach bands and lateral inhibition
  4. Limulus-the horseshoe crab
  5. Sharpening, the Laplacian and lateral inhibition in neural networks (PostScript file)
  6. Laplacian edges
  7. Unsharp masking
  8. More edge detection

Spatial Moments

  1. Moments of univariate distributions, skew and kurtosis (PostScript file)
  2. Basics on multivariate moments (PostScript file)

Medial Axis Transformations

  1. Distance between sets
  2. Medial Axis (prairie-fire transformation)
  3. Skeletons (PostScript file)
  4. Skeletonization software
  5. Minkowski metrics
  6. Distance transforms
  7. Skeleton clean-up via distance transforms
  8. Medial axes via distance transforms
  9. Medial axis transform
  10. Medial axis in 3D with applications

Topological Feature Extraction

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

Processing Line Drawings

  1. Basics of Chain Coding (PostScript file)

Detection of Structure in Noisy Pictures and Dot Patterns

  1. Point-to-curve transformations (Hough transform)
  2. Hough Transform tutorial
  3. Hough Transform demo on satellite photos
  4. Hough Transform home page (and software)
  5. Hough Transform publications
  6. More Hough Transform code
  7. Interactive histogram with Java applet
  8. Proximity graphs and perception
  9. Delaunay Triangulations and Voronoi diagrams
  10. The shape of a set of points
  11. Relative neighbourhood graphs
  12. Sphere-of-influence graphs
  13. Alpha shapes
  14. Beta skeletons

Neural Networks and Bayesian Decision Theory

  1. Neural Network Basics (FAQ's)
  2. Neural Network Basics (with Java)
  3. Formal neurons, linear machines & perceptrons
  4. Introduction to Probability and Statistics
  5. Basics of Statistical Pattern Recognition
  6. Minimum risk classification
  7. Minimum error classification
  8. Discriminant functions (linear, quadratic, polynomial)
  9. The multivariate Gaussian probability density function
  10. Mahalanobis distance classifiers
  11. Parametric decision rules
  12. Learning from examples

Independence of Measurements, Redundancy, and Synergism

  1. Independence in the discrete case
  2. Conditional and unconditional independence
  3. Dependence and correlation
  4. The best k measurements are not the k best
  5. Information theory and feature evaluation criteria
  6. Feature selection methods
  7. Models of spatial dependence between features

Neural Networks and Non-parametric Learning

  1. General Learning Resources
  2. Perceptrons
  3. Non-parametric training of linear machines
  4. Error-correction procedures
  5. The fundamental learning theorem
  6. Multi-layer networks
  7. Reinforcement Learning - An Intercative Tutorial

Estimation of Parameters and Classifier Performance

  1. Properties of estimators
  2. Dimensionality and sample size
  3. Estimation of the probability of misclassification

Nearest Neighbor Decision Rules

  1. The k-nearest neighbor rule
  2. Efficient search methods for nearest neighbors
  3. Decreasing space requirements
  4. Editing training sets (compressed PostScript file)
  5. Error bounds
  6. Nearest neighbor computation software

Using Contextual Information in Pattern Recognition

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

Cluster Analysis and Unsupervised Learning

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

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