Dietrich Paulus Joachim Hornegger. Pattern Recognition of Images and Speech in C++

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1 Dietrich Paulus Joachim Hornegger Pattern Recognition of Images and Speech in C++

2 To Dorothea, Belinda, and Dominik In the text we use the following names which are protected, trademarks owned by a company or should be emphasized otherwise: GNU, HP, AT&T, Unix, PostScript L A TEX, HTK, Entropic, Corel Draw.

3 i Part I Introductions 5 1 Pattern Recognition Images and Sound Applications of Pattern Recognition Environment, Problem Domain, and Patterns Characterization of Pattern Recognition Speech Recording Video Cameras and Projections From Continuous to Digital Signals Sampling Theorem in Practice Visualization and Sound Generation From C to C Syntax Notation Principle of C++ Compilation Function Calls and Arguments Declaration and Definition of Variables Unix File Access via Standard Functions Numeric Expressions Main Program Function Definition Scope and Lifetime Software Development Software for Pattern Recognition Software Development and Testing Modular and Structured Programming Comments and Program Layout Documentation Teamwork Efficiency Tools for Software Development PUMA Control and Data Structures Structures Enumerations Scope Resolution Unions Bit and Shift Operations and Bit Fields Logical Values and Conditionals Loops

4 ii 4.8 Switches Exception Handling Arrays and Pointers Vectors and Matrices Pointers Vectors vs. Pointers Pointer Operations and Allocation Pointer to Structures Strings Pointer and Array Arguments Pointer to Pointer Command Line Arguments Classification and Pattern Analysis Classification Preprocessing Feature Extraction Analysis Image Segmentation Speech Segmentation Pattern Understanding Active Vision and Real Time Processing Software Systems C++ as a better C Type Declaration Type Conversion for Pointers Type Specifiers and Variable Declaration Type Safe Linkage Overloaded Function Names Return Value and Arguments Macros and Inline Functions Function Pointers Comma Operator and Conditional Expressions Statistics for Pattern Recognition Axioms Discrete Random Variables Continuous Random Variables Mean and Variance Moments of a Distribution Random Vectors

5 iii 8.7 Independence and Marginal Densities Statistical Features and Entropy Signal to Noise Ratio

6 iv Part II Object Oriented Pattern Recognition Object Oriented Programming Object Oriented Software Techniques Basic Concepts Data Abstraction and Modules Unified Modeling Language Inheritance Abstract Classes Object Oriented Classification Polymorphism C++ as an Object Oriented Language Class Libraries Classes in C Methods and ADT s Class Declarations Object Construction Destruction of Objects Operators User Defined Conversion Advanced Methods and Constructors Vector Class Class Design Representation of Signals Array Class Templates Images External Data Formats Binary Images Color Images Subimages Matrix Operations Speech Signal Class Fourier Transform Introductory Considerations Fourier Series Fourier Transform Discrete Fourier Transform Complex Number Class Inverse Discrete Fourier Transform

7 v 12.7 Fourier Transforms of Speech Signals Fast Fourier Transform D Fourier Transform Inheritance for Classes Motivation and Syntax Access to Members of Base Class Construction and Destruction Pointer to Objects Virtual Functions Abstract Classes Image Class Hierarchy Multiple Inheritance Implementation Issues Edge Images Strategies Discrete Derivatives of Intensity Functions Mask Operators Discrete Directions Edge Class Edge Images Robert s Cross Second Derivative Color Edge Operators Class Libraries Stream Input and Output National Institutes of Health Class Library Static Class Members Input and Output for Objects NIHCL Application Classes NIHCL Collection Classes Memory Allocation Standard Template Library Templates vs. Inheritance

8 vi Part III Object Oriented Image Processing Hierarchy of Picture Processing Objects General Structure HIPPOS Object Images and Matrices Chain Code Class Edges Polygon Representation Atomic Objects Segmentation Objects External Representation An Image Analysis System Data Flow Design of ANIMALS Display and Capture Geometric Distortions Polymorphic Image Processing Efficiency Command Line Options Graphical User Interfaces Image Segmentation Program Synthetic Signals and Images Synthetic Sound Geometric Patterns Examples in C Pixel Noise Gaussian Noise Salt and Pepper Noise D Views of 3 D Polyhedral Objects Single Stereo Images Textures Filtering and Smoothing Signals Linear Filters Rank Order Operations Edge Preserving Smoothing Nearest Neighbor Averaging Conditional Average Filter Linear Reconstruction Elimination of Noisy Image Rows

9 vii 19.8 Resolution Hierarchies Image Operator Hierarchy Histogram Algorithms Discriminant Analysis Threshold Histogram Entropy Thresholding Multi thresholding Global Histogram Equalization Local Histogram Equalization Look up Table Transformation Histogram Class Color Quantization Histogram Back Projection Edges and Lines More Edge Detectors Edge Thinning Line Detection Hysteresis Thresholds Closing of Gaps Zero Crossings in Laplace Images Hough Transform Circle Detection Optimal Line Detection Chain Codes Smoothing Digital Linear Lines Neighborhood Contours in Binary Images Length, Area, and Similarity Intersections Rotation Conversion Corners of Chain Codes

10 viii Part IV Speech and Pattern Analysis Spatial and Spectral Features Different Types of Features Frames and Blocks Spatial Features Short Time Fourier Analysis and Spectral Features Cepstral Features Mel Spectral and Cepstral Features Linear Predictive Coding Model Spectrum and Cepstrum Implementation Issues Numerical Pattern Classification General Notes on Classifiers Design of Classifiers Linear Discriminants Polynomial Classifiers Bayesian Classifiers Properties of Bayesian Classifiers From Bayesian to Geometric Classifiers Nearest Neighbor Classifier Implementation of Classifiers Speech Recognition Classification of Speech Signals Dynamic Time Warping Mixture Densities Hidden Markov Models Topological and Statistical Variations Generalized Hidden Markov Models Incomplete Data Estimation Learning from Multiple Observations An Object Oriented Implementation of Hidden Markov Models Advanced Topics Segmentation into Lines and Arcs Stereo Images Region Segmentation Active Vision Semantic Networks Statistical Object Recognition and Localization Artificial Neural Networks

11 ix 26.8 Advanced C++ Features The Future of C

12 x Part V Appendix 423 A Software Development Tools 425 A.1 Groups and ID s with Unix A.2 Program Building with make A.3 The Use of Libraries A.4 Version and Access Control with rcs A.5 Teamwork B Source Code and Tools 431 B.1 List of Tools B.2 How to get the sources B.3 X B.4 Slides B.5 Addresses B.6 Headers and Source Files C Formulas 435 C.1 Lookup Table Transformation C.2 Marginal Density C.3 Identity C.4 Property of the Function C.5 Lagrange Multiplier D Notation 441 Bibliography 443 Figures 457 Tables 463 List of Programs 465 Index 467

COMPUTER AND ROBOT VISION

VOLUME COMPUTER AND ROBOT VISION Robert M. Haralick University of Washington Linda G. Shapiro University of Washington A^ ADDISON-WESLEY PUBLISHING COMPANY Reading, Massachusetts Menlo Park, California