Applied Pattern Recognition: A Practical Introduction to Image and Speech Processing in C++

by Paulus, Dietrich W. R.; Hornegger, Joachim

• ISBN13: 9783528155582
• ISBN10: 3528155582
• Edition: 2nd
• Format: Paperback
• Copyright: 1999-06-01
• Publisher: Morgan Kaufmann Pub
• More Book Details
• Purchase Benefits

Summary

This book demonstrates the efficiency of the C++ programming language in the realm of pattern recognition and pattern analysis. It introduces the basics of software engineering, image and speech processing, as well as fundamental mathematical tools for pattern recognition. Step by step the C++ programming language is discribed. Each step is illustrated by examples based on challenging problems in image und speech processing. Particular emphasis is put on object-oriented programming and the implementation of efficient algorithms. The book proposes a general class hierarchy for image segmentation. The essential parts of an implementation are presented. An object-oriented system for speech classification based on stochastic models is described.

Table of Contents

Part I Introductions	3	(94)
Pattern Recognition	5	(12)
Images and Sound	5	(1)
Applications of Pattern Recognition	6	(1)
Environment, Problem Domain, and Patterns	7	(1)
Characterization of Pattern Recognition	8	(1)
Speech Recording	9	(1)
Video Cameras and Projections	10	(2)
From Continuous to Digital Signals	12	(3)
Sampling Theorem in Practice	15	(1)
Visualization and Sound Generation	15	(2)
From C to C++	17	(14)
Syntax Notation	17	(1)
Principle of C++ Compilation	18	(2)
Function Calls and Arguments	20	(1)
Declaration and Definition of Variables	21	(1)
Unix--File Access via Standard Functions	22	(2)
Numeric Expressions	24	(1)
Main Program	25	(1)
Function Definition	26	(1)
Scope and Lifetime	27	(4)
Software Development	31	(10)
Software for Pattern Recognition	31	(1)
Software Development and Testing	32	(2)
Modular and Structured Programming	34	(1)
Comments and Program Layout	35	(1)
Documentation	35	(1)
Teamwork	36	(1)
Efficiency	37	(1)
Tools for Software Development	37	(1)
PUMA	38	(3)
Control and Data Structures	41	(10)
Structures	41	(1)
Enumerations	41	(1)
Scope Resolution	42	(1)
Unions	43	(1)
Bit-- and Shift--Operations and Bit--Fields	43	(2)
Logical Values and Conditionals	45	(1)
Loops	46	(1)
Switches	47	(1)
Exception Handling	48	(3)
Arrays and Pointers	51	(10)
Vectors and Matrices	51	(1)
Pointers	52	(2)
Vectors vs. Pointers	54	(1)
Pointer Operations and Allocation	55	(1)
Pointer to Structures	56	(1)
Strings	57	(1)
Pointer and Array Arguments	58	(1)
Pointer to Pointer	58	(1)
Command Line Arguments	59	(2)
Classification and Pattern Analysis	61	(12)
Classification	61	(1)
Preprocessing	62	(1)
Feature Extraction	63	(1)
Analysis	64	(1)
Image Segmentation	64	(3)
Speech Segmentation	67	(1)
Pattern Understanding	68	(1)
Active Vision and Real Time Processing	69	(1)
Software Systems	70	(3)
C++ as a better C	73	(12)
Type Declaration	73	(1)
Type Conversion for Pointers	73	(1)
Type Specifiers and Variable Declaration	74	(2)
Type--Safe Linkage	76	(2)
Overloaded Function Names	78	(1)
Return Value and Arguments	78	(2)
Macros and Inline Functions	80	(1)
Function Pointers	80	(2)
Comma Operator and Conditional Expressions	82	(3)
Statistics for Pattern Recognition	85	(12)
Axioms	85	(1)
Discrete Random Variables	86	(1)
Continuous Random Variables	87	(2)
Mean and Variance	89	(1)
Moments of a Distribution	90	(1)
Random Vectors	91	(2)
Independence and Marginal Densities	93	(1)
Statistical Features and Entropy	94	(1)
Signal--to--Noise Ratio	95	(2)
Part II Object--Oriented Programming	97	(100)
Object--Oriented Programming	99	(10)
Object--Oriented Software Techniques	99	(1)
Basic Concepts	100	(1)
Data Abstraction and Modules	101	(1)
Inheritance and Templates	102	(2)
Abstract Classes	104	(1)
Object--Oriented Classification	105	(1)
Polymorphism	106	(1)
Object--Oriented Programming Languages	106	(1)
Class Libraries	107	(2)
Classes in C++	109	(14)
Methods and ADT's	109	(2)
Class Declarations	111	(1)
Object Construction	112	(2)
Destruction of Objects	114	(1)
Operators	114	(2)
User--Defined Conversion	116	(1)
Advanced Methods and Constructors	117	(1)
Vector Class	118	(2)
Class Design	120	(3)
Representation of Signals	123	(14)
Array Class	123	(2)
Templates	125	(2)
Images	127	(2)
External Data Formats	129	(1)
Binary Images	129	(1)
Color Images	130	(2)
Subimages	132	(1)
Matrix Operations	133	(2)
Speech Signal Class	135	(2)
Fourier Transform	137	(18)
Introductory Considerations	137	(1)
Fourier Series	138	(2)
Fourier Transform	140	(4)
Discrete Fourier Transform	144	(1)
Complex Number Class	145	(2)
Inverse Discrete Fourier Transform	147	(1)
Fourier Transforms of Speech Signals	148	(1)
Fast Fourier Transform	149	(1)
2--D Fourier Transform	150	(5)
Inheritance for Classes	155	(12)
Motivation and Syntax	155	(1)
Access to Members of Base Class	156	(1)
Construction and Destruction	157	(2)
Pointer to Objects	159	(1)
Virtual Functions	160	(2)
Abstract Classes	162	(1)
Image Class Hierarchy	163	(1)
Multiple Inheritance	163	(2)
Implementation Issues	165	(2)
Edge Images	167	(14)
Strategies	167	(1)
Discrete Derivatives of Intensity Functions	168	(2)
Mask Operators	170	(1)
Discrete Directions	171	(1)
Edge Class	172	(1)
Edge Images	173	(2)
Robert's Cross	175	(1)
Second Derivative	176	(1)
Color Edge Operators	177	(4)
Class Libraries	181	(16)
Stream Input and Output	181	(2)
National Institutes of Health Class Library	183	(1)
Static Class Members	183	(4)
Input and Output for Objects	187	(1)
NIHCL Application Classes	188	(2)
NIHCL Collection Classes	190	(2)
Memory Allocation	192	(2)
Standard Template Library	194	(1)
Templates vs. Inheritance	195	(2)
Part III Object--Oriented Systems	197	(96)
Hierarchy of Picture Processing Objects	199	(14)
General Structure	199	(1)
HIPPOS Object	200	(2)
Images and Matrices	202	(1)
Chain Code Class	203	(1)
Edges	204	(2)
Polygon Representation	206	(1)
Atomic Objects	207	(2)
Segmentation Objects	209	(1)
External Representation	210	(3)
An Image Analysis System	213	(12)
Data Flow	213	(1)
Design of ANIMALS	213	(1)
Display and Capture	214	(2)
Geometric Distortions	216	(1)
Polymorphic Image Processing	217	(2)
Efficiency	219	(1)
Command Line Options	219	(2)
Graphical User Interfaces	221	(1)
Image Segmentation Program	221	(4)
Synthetic Signals and Images	225	(10)
Synthetic Sound	225	(1)
Geometric Patterns	226	(1)
Examples in C++	227	(1)
Pixel Noise	227	(1)
Gaussian Noise	228	(1)
Salt--and--Pepper Noise	229	(1)
2--D Views of 3--D Polyhedral Objects	230	(1)
Single Stereo Images	231	(1)
Textures	232	(3)
Filtering and Smoothing Signals	235	(12)
Linear Filters	235	(2)
Rank Order Operations	237	(1)
Edge Preserving Smoothing	238	(1)
K Nearest Neighbor Averaging	239	(1)
Conditional Average Filter	240	(1)
Linear Reconstruction	240	(1)
Elimination of Noisy Image Rows	241	(2)
Resolution Hierarchies	243	(1)
Image Operator Hierarchy	244	(3)
Histogram Algorithms	247	(14)
Discriminant Analysis Threshold	247	(2)
Histogram Entropy Thresholding	249	(1)
Multi--thresholding	250	(3)
Global Histogram Equalization	253	(1)
Local Histogram Equalization	254	(1)
Look up Table Transformation	254	(2)
Histogram Class	256	(1)
Color Quantization	256	(1)
Histogram Back--Projection	257	(4)
Edges and Lines	261	(22)
More Edge Detectors	261	(2)
Edge Thinning	263	(5)
Line Detection	268	(2)
Hysteresis Thresholds	270	(3)
Closing of Gaps	273	(1)
Zero--Crossings in Laplace--Images	274	(1)
Hough Transform	275	(2)
Circle Detection	277	(4)
Optimal Line Detection	281	(2)
Chain Codes	283	(10)
Smoothing	283	(1)
Digital Linear Lines	284	(1)
Neighborhood	285	(1)
Contours in Binary Images	286	(1)
Length, Area, and Similarity	287	(2)
Intersections	289	(1)
Rotation	289	(1)
Conversion	290	(1)
Corners of Chain Codes	291	(2)
Part IV Speech and Pattern Analysis	293	(74)
Spatial and Spectral Features	295	(20)
Different Types of Features	295	(1)
Frames and Blocks	296	(2)
Spatial Features	298	(3)
Short Time Fourier Analysis and Spectral Features	301	(4)
Cepstral Features	305	(2)
Mel Spectral and Cepstral Features	307	(1)
Linear Predictive Coding	308	(4)
Model Spectrum and Cepstrum	312	(1)
Implementation Issues	313	(2)
Numerical Pattern Classification	315	(18)
General Notes on Classifiers	316	(1)
Design of Classifiers	317	(1)
Linear Discriminants	318	(4)
Polynomial Classifiers	322	(1)
Bayesian Classifiers	323	(2)
Properties of Bayesian Classifiers	325	(1)
From Bayesian to Geometric Classifiers	326	(2)
Nearest Neighbor Classifiers	328	(2)
Implementation of Classifiers	330	(3)
Speech Recognition	333	(26)
Classification of Speech Signals	333	(1)
Dynamic Time Warping	334	(6)
Mixture Densities	340	(4)
Hidden Markov Models	344	(3)
Topological and Statistical Variations	347	(2)
Generalized Hidden Markov Models	349	(1)
Incomplete Data Estimation	350	(2)
Learning from Multiple Observations	352	(4)
An Object--Oriented Implementation of Hidden Markov Models	356	(3)
Advanced Topics	359	(8)
Image Analysis	359	(1)
Stereo Images	360	(1)
Region Segmentation	360	(1)
Speech Understanding	361	(1)
Knowledge Representation for Pattern Understanding	361	(1)
Statistical and Neural Pattern Recognition	362	(1)
Advanced C++ Features	363	(2)
Java	365	(1)
The Future of C++	365	(2)
Part V Appendix	367	(15)
A Software Development Tools	369	(5)
A.1 Groups and ID's with Unix	369	(1)
A.2 Program Building with make	370	(1)
A.3 The Use of Libraries	371	(1)
A.4 Version and Access Control	372	(1)
A.5 Teamwork	373	(1)
B Source Code and Tools	374	(3)
B.1 List of Tools	374	(1)
B.2 How to get the sources	374	(1)
B.3 X11	375	(1)
B.4 Slides	375	(1)
B.5 Addresses	375	(1)
B.6 Headers and Source Files	375	(2)
C Formulas	377	(4)
C.1 Lookup Table Transformation	377	(1)
C.2 Marginal Density	377	(1)
C.3 Identity	378	(1)
C.4 Property of the H--Function	378	(1)
C.5 Lagrange Multiplier	379	(2)
D Notation	381	(1)
Bibliography	382	(11)
Figures	393	(5)
Tables	398	(1)
List of Programs	399	(1)
Index	400