Contents

• Contents
• Basic Concepts
  • Measurement and Representation
    • From objects to points in space
    • Telling the guys from the gals
    • Paradigms
  • Decisions, decisions..
    • Metric Methods
    • Neural Net Methods (Old Style)
    • Statistical Methods
      • Parametric
      • Non-parametric
    • CART et al
  • Clustering: supervised v unsupervised learning
  • Dynamic Patterns
  • Structured Patterns
  • Alternative Representations
    • Strings, propositions, predicates and logic
    • Fuzzy Thinking
    • Robots
  • Summary of this chapter
  • Exercises
  • Bibliography
• Image Measurements
  • Preliminaries
    • Image File Formats
  • Generalities
  • Image segmentation: finding the objects
    • Mathematical Morphology
    • Little Boxes
    • Border Tracing
    • Conclusions on Segmentation
  • Measurement Principles
    • Issues and methods
    • Invariance in practice
  • Measurement practice
    • Quick and Dumb
    • Scanline intersections and weights
    • Moments
    • Zernike moments and the FFT
      • Historical Note
    • Masks and templates
    • Invariants
    • Chaincoding
  • Syntactic Methods
  • Summary of OCR Measurement Methods
  • Other Kinds of Binary Image
  • Greyscale images of characters
    • Segmentation: Edge Detection
  • Greyscale Images in general
    • Segmentation
    • Measuring Greyscale Images
    • Quantisation
    • Textures
  • Colour Images
    • Generalities
    • Quantisation
    • Edge detection
    • Markov Random Fields
    • Measurements
  • Spot counting
  • IR and acoustic Images
  • Quasi-Images
  • Dynamic Images
  • Summary of Chapter Two
  • Exercises
  • Bibliography
• Statistical Ideas
  • History, and Deep Philosophical Stuff
    • The Origins of Probability: random variables
    • Histograms and Probability Density Functions
    • Models and Probabilistic Models
  • Probabilistic Models as Data Compression Schemes
    • Models and Data: Some models are better than others
  • Maximum Likelihood Models
    • Where do Models come from?
  • Bayesian Methods
    • Bayes' Theorem
    • Bayesian Statistics
    • Subjective Bayesians
  • Minimum Description Length Models
    • Codes: Information theoretic preliminaries
    • Compression for coin models
    • Compression for pdfs
    • Summary of Rissanen Complexity
  • Summary of the chapter
  • Exercises
  • Bibliography
• Decisions: Statistical methods
  • The view into ${\fam11\tenbbb R}^n$
  • Computing PDFs: Gaussians
    • One Gaussian per cluster
      • Dimension 2
    • Lots of Gaussians: The EM algorithm
      • The EM algorithm for Gaussian Mixture Modelling
    • Other Possibilities
  • Bayesian Decision
    • Cost Functions
    • Non-parametric Bayes Decisions
    • Other Metrics
  • How many things in the mix?
    • Overhead
    • Example
    • The Akaike Information Criterion
    • Problems with EM
  • Summary of Chapter
  • Exercises
  • Bibliography
• Decisions: Neural Nets(Old Style)
  • History: the good old days
    • The Dawn of Neural Nets
    • The death of Neural Nets
    • The Rebirth of Neural Nets
    • The End of History
  • Training the Perceptron
    • The Perceptron Training Rule
  • Committees
    • Committees and XOR
    • Training Committees
    • Capacities of Committees: generalised XOR
    • Four Layer Nets
    • Building up functions
  • Smooth thresholding functions
    • Back-Propagation
    • Mysteries of Functional Analysis
    • Committees vs Back-Propagation
  • Compression: is the model worth the computation?
  • Other types of (Classical) net
    • General Issues
    • The Kohonen Net
    • Probabilistic Neural Nets
    • Hopfield Networks
      • Introduction
      • Network Characteristics
      • Network Operation
      • The Network Equations
      • Theory of the Network
      • Applications
    • The Boltzmann Machine
      • Introduction
      • Simulated Annealing
      • Network Characteristics
      • Network Operation
      • Theory of the Network
      • Applications
    • Bidirectional Associative Memory
      • Introduction
      • Network Characteristics
      • Network Operation
      • The Network Equations
      • Theory of the Network
      • Applications
    • ART
      • Introduction
      • Network Characteristics
      • Network Operation
      • Theory of the Network
      • Applications
    • Neocognitron
      • Introduction
      • Network Structure
      • The Network Equations
      • Training the Network
      • Applications
    • References
    • Quadratic Neural Nets: issues
  • Summary of Chapter Five
  • Exercises
  • Bibliography
• Continuous Dynamic Patterns
  • Automatic Speech Recognition
    • Talking into a microphone
    • Traditional methods: VQ and HMM
      • The Baum-Welch and Viterbi Algorithms for Hidden Markov Models
    • Network Topology and Initialisation
    • Invariance
    • Other HMM applications
    • Connected and Continuous Speech
  • Filters
    • Linear Systems
    • Moving Average Filters
    • Autoregressive Time Series
    • Linear Predictive Coding or ARMA modelling
    • Into ${\fam11\tenbbb R}^n$
    • States
    • Wiener Filters
    • Adaptive Filters, Kalman Filters
  • Fundamentals of dynamic patterns
  • Exercises
  • Bibliography
• Discrete Dynamic Patterns
  • Alphabets, Languages and Grammars
    • Definitions and Examples
    • ReWrite Grammars
    • Grammatical Inference
    • Inference of ReWrite grammars
  • Streams, predictors and smoothers
  • Chunking by Entropy
  • Stochastic Equivalence
  • Quasi-Linguistic Streams
  • Graphs and Diagram Grammars
  • Exercises
  • Bibliography
• Syntactic Pattern Recognition
  • Precursors
  • Linear Images
  • Curved Elements
  • Parameter Regimes
  • Invariance:
    Classifying Transformations
  • Intrinsic and Extrisic Chunking (Binding)
  • Backtrack
  • Occlusion and other metric matters
  • Neural Modelling
    • Self-Tuning Neurons
    • Geometry and Dynamics
    • Extensions to Higher Order Statistics
    • Layering
  • Summary of Chapter
  • Exercises
  • Bibliography