Applied Pattern Recognition: A Practical Introduction to Image and Speech Processing in C++ von Joachim Hornegger (Autor), Dietrich W. R. Paulus Algorithms and Implementation in C++ Advanced Studies of Computer Science

Joachim Hornegger Dietrich W. R. Paulus

ISBN 10: 3528054913 / ISBN 13: 9783528054915

Editore: Vieweg+Teubner Verlag Springer Fachmedien Wiesbaden, 1995

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Bildverarbeitung C++ Programmiersprache Efficient Algorithms Informatik EDV Image Processing Inheritance Mustererkennung Pattern Analysis pattern recognition Program Design programming software development Speech processing Spracherkennung statistical pattern recognition Provides an introduction to applications and problems from the field of pattern recognition. Basic topics and the mathematical background of image and speech processing are described. Algorithms and data structures for filtering, feature extraction, segmentation and classification are discussed.This work provides an instructive into applications and problems from the broad field of pattern recognition. It describes basic topics and the required mathematical background of image and speech processing. Algorithms and data structures for filtering, feature extraction, segmentation and classification are discussed, introducing and demonstrating different C++ concepts. The practice of object-oriented programming is illustrated by a step-wise development of a complete class library for image processing. Part 1 Introductions: pattern recognition; from C to C++; software development; expressions, statements, functions; classification and pattern analysis; arrays and pointers; statistics for pattern recognition; C++ as a better C. Part 2 Object-oriented pattern analysis: object-oriented programming; classes in C++; intensity images; inheritance in C++-classes; edge detection and edge images; class libraries; hierarchy of picture processing objects; spectral features and speech processing. Part 3 Pattern recognition algorithms: an image analysis system; synthetic signals and images; filtering and smoothing signals; histogram algorithms; edge images; line detection algorithms; chain codes. Appendices: basics of C++; software development tools; source codes and tools. Ein ganz großer Vorteil dieses Buches ist, dass die Thematik nicht nur auf einer abstrakten Ebene beschrieben wird, sondern dass im Buch immer wieder die Umsetzung der Aussagen in C++ gezeigt wird. neben einer Einführung in C++ werden die Algorithmen und Techniken immer wieder auch als Programm vorgestellt. Dadurch wird der Schritt von der Theorie - das heißt, dem als mathematische Formel beschriebenen Algorithmus - hin zur Praxis (dem Computerprogramm) durchgängig." Elektronik This book demonstrates the efficiency of the C++ programming language in the realm of pattern recognition and pattern analysis. For this 3rd edition, new features of the C++ language were integrated and their relevance for image and speech processing is discussed. The description of the STL library was extended and STL is now used in many places in the book. The chapters 23 and 24 can now be used to build complete systems and give hints for further extensions.The book is divided into 4 parts. After an introduction to programming and software engineering, the required basics of image and speech processing are given. Statistics and mathematics is explained as required. The second part treats C++ as an object-oriented language and introduces it thru examples. In the third part the examples of the first and second part are combined to form an image segmentation and a word recognition system. All relevant features of the C++ language are thus introduced with direct relation to the tasks in image and speech processing. "Die Behandlung der C++-Konstukte ist wirklich anwendungsnah." Prof. Dr. Michael Schenke, FH Merseburg "// sehr umfassend - anschaulich, klar, gut nachvollziehbar." Prof. Dr.-Ing. Hans Peter Kölzer, HAW Hamburg "Dieses Buch werde ich meinen Studenten empfehlen, weil es einen sehr anschaulichen und motivierenden Einstieg in die Mustererkennung bietet." Prof. Dr. Hans-Peter Weber, FH Darmstadt Concerning 3rd edition: "This book is a beautifully arranged marriage of state-of-the-art pattern recognition theory and of programming theory and practice. The elegant and thorough treatment of modern aspects of speech and video processin.

Codice articolo BN27984

Dati bibliografici

• Titolo: Applied Pattern Recognition: A Practical Introduction to Image and Speech Processing in C++ von Joachim Hornegger (Autor), Dietrich W. R. Paulus Algorithms and Implementation in C++ Advanced Studies of Computer Science
• Editore: Vieweg+Teubner Verlag Springer Fachmedien Wiesbaden
• Data di pubblicazione: 1995
• Lingua: Inglese
• ISBN 10: 3528054913
• ISBN 13: 9783528054915
• Rilegatura: Softcover
• Edizione: 1995.
• Condizione: gut

Descrizione articolo

Riassunto

Parts of this text were used for several years by students in a one~term under­ graduate course in computer science. The students had to prepare projects in small groups (2~4 students).1 This book emphasizes practical experience with image processing. It offers a comprehensive study of • image processing and image analysis, • basics of speech processing, • object~oriented programming, • software design, • and programming in C++. The book is divided into four parts. In the first part we introduce image processing, image analysis, programming tools, and the basics of C++. In the second part we describe object~oriented programming in general and the possible applications of object~oriented concepts in C++. Several appli­ cations of object~oriented programming for image processing are discussed as well. The new features of C++ are introduced entirely through the use of examples. We cover the proper representation of the data that is a result of pattern analysis as well. The third part describes a complete system for image segmentation. Some of the material covered refers to the exercises found in the first and second parts: this verifies our belief that an image segmentation system of programs can be developed while simultaneously acquainting others to C++. We combine the data representation described in the second part with the algorithms that use and manipulate them here in the third part.

Contenuti

I Introductions.- 1 Pattern Recognition.- 1.1 Images and Sound.- 1.2 Applications of Pattern Recognition.- 1.3 Environment, Problem Domain, and Patterns.- 1.4 Characterization of Pattern Recognition.- 1.5 Recording of Speech Signals.- 1.6 Video Cameras and Projections.- 1.7 From Continuous to Digital Signals.- 1.8 Sampling Theorem in Practice.- 1.9 Visualization and Sound Generation.- 2 From C to C++.- 2.1 Syntax Notation.- 2.2 Principle of C++ Compilation.- 2.3 Function Calls and Arguments.- 2.4 Declaration and Definition of Variables.- 2.5 Unix-File Access via Standard Functions.- 2.6 Formatted Input and Output.- 2.7 Main Program.- 2.8 Preprocessor Directives.- 2.9 Conditional Compilation.- 3 Software Development.- 3.1 Software for Pattern Recognition.- 3.2 Principles of Software Development.- 3.3 Modular and Structured Programming.- 3.4 Comments and Program Layout.- 3.5 Documentation.- 3.6 Teamwork.- 3.7 Efficiency.- 3.8 Tools for Software Development with Unix.- 3.9 PUMA.- 4 Expressions, Statements, Functions.- 4.1 Instructions and Expressions.- 4.2 Logical Values and Conditionals.- 4.3 Function Definition.- 4.4 Loops.- 4.5 Declarations and Scope.- 4.6 Switches.- 4.7 Linkage.- 4.8 Programming with Modules.- 4.9 Control Structures.- 5 Classification and Pattern Analysis.- 5.1 Classification.- 5.2 Preprocessing.- 5.3 Feature Extraction.- 5.4 Analysis.- 5.5 Image Segmentation.- 5.6 Speech Segmentation.- 5.7 Pattern Understanding.- 5.8 Active Vision and Real Time Processing.- 5.9 Top-Level Loop for Speech Analysis.- 6 Arrays and Pointers.- 6.1 Vectors and Matrices.- 6.2 Pointers.- 6.3 Vectors vs. Pointers.- 6.4 Vector Initialization.- 6.5 Strings.- 6.6 Pointers Operations and Allocation.- 6.7 Pointer and Array Arguments.- 6.8 Pointer to Pointer.- 6.9 Main Function Arguments.- 7 Statistics for Pattern Recognition.- 7.1 Axioms.- 7.2 Discrete Random Variables.- 7.3 Continuous Random Variables.- 7.4 Mean and Variance.- 7.5 Moments of a Distribution.- 7.6 Random Vectors.- 7.7 Statistical Features and Entropy.- 7.8 Signal-to-Noise Ratio.- 7.9 Histograms.- 8 C++ as a better C.- 8.1 Type Declaration.- 8.2 Type Conversion for Pointers.- 8.3 Bit- and Shift-Operations.- 8.4 Type Specifiers and Variable Declaration.- 8.5 Type-Safe Linkage.- 8.6 Overloaded Function Names.- 8.7 Return Value and Arguments.- 8.8 Macros and Inline Functions.- 8.9 Function Pointers.- II Object-Oriented Pattern Analysis.- 9 Object-Oriented Programming.- 9.1 Object-Oriented Software Techniques.- 9.2 Basic Concepts.- 9.3 Data Abstraction and Modules.- 9.4 Inheritance.- 9.5 Abstract Classes.- 9.6 Object-Oriented Classification.- 9.7 Polymorphism.- 9.8 Other Object-Oriented Concepts.- 9.9 Class Libraries.- 10 Classes in C++.- 10.1 Structures.- 10.2 Methods and ADT’s.- 10.3 Class Declarations.- 10.4 Object Construction.- 10.5 Destruction of Objects.- 10.6 Overloaded Operators.- 10.7 Advanced Methods and Constructors.- 10.8 Vector Class.- 10.9 Class Design.- 11 Intensity Images.- 11.1 Array Class.- 11.2 Templates in C++.- 11.3 Images.- 11.4 External Data Formats.- 11.5 Binary Images.- 11.6 Color Images.- 11.7 Sub Images.- 11.8 Image Transformation and Registration.- 11.9 Neighborhood.- 12 Inheritance in C++-Classes.- 12.1 Motivation and Syntax.- 12.2 Base Class Access.- 12.3 Construction and Destruction.- 12.4 Pointer to Objects.- 12.5 Virtual Functions.- 12.6 Abstract Classes.- 12.7 Image Class Hierarchy.- 12.8 Multiple Inheritance.- 12.9 Implementation Issues.- 13 Edge Detection and Edge Images.- 13.1 Motivation.- 13.2 Strategies.- 13.3 Discrete Derivative of the Intensity.- 13.4 Sobel and Prewitt Operator.- 13.5 Bit Fields in C++.- 13.6 Unions in C++.- 13.7 Edge Class.- 13.8 Edge Images.- 13.9 Color Edge Operators.- 14 Class Libraries.- 14.1 Stream Input and Output.- 14.2 NIH Class Library.- 14.3 Dynamic Class Descriptions.- 14.4 Static Class Members.- 14.5 Input and Output for Objects.- 14.6 Strings.- 14.7 Container Classes.- 14.8 Time and Date.- 14.9 More Classes.- 15 Hierarchy of Picture Processing Objects.- 15.1 General Structure.- 15.2 Hippos Object.- 15.3 Images and Matrices.- 15.4 Chain Code Class.- 15.5 Enumerations and Scope Resolution.- 15.6 Polygonal Representation.- 15.7 Atomic Objects.- 15.8 Segmentation Objects.- 15.9 External Representation.- 16 Spectral Features and Speech Processing.- 16.1 Fourier Series and Fourier Transform.- 16.2 Discrete Fourier Transform.- 16.3 Fast Fourier Transform.- 16.4 2D Fourier Transform.- 16.5 Short time Fourier analysis.- 16.6 Linear Predictive Coding.- 16.7 Dynamic Time Warping.- 16.8 Hidden Markov Models.- 16.9 Different Types of Hidden Markov Modells.- III Pattern Recognition Algorithms.- 17 An Image Analysis System.- 17.1 Data Flow.- 17.2 Design of ANIMALS.- 17.3 XDR.- 17.4 Display and Capture.- 17.5 Graphical User Interfaces.- 17.6 Geometric Distortions.- 17.7 Polymorphic image processing.- 17.8 Efficiency.- 17.9 Image Segmentation Program.- 18 Synthetic Signals and Images.- 18.1 Synthetic Sound.- 18.2 Geometric Patterns.- 18.3 Pixel Noise.- 18.4 Gaussian Noise.- 18.5 Salt-and-Pepper Noise.- 18.6 Different Views of a 3D Polyhedral Object.- 18.7 Digits and Letters.- 18.8 Single Stereo Images.- 18.9 Spectrogram.- 19 Filtering and Smoothing Signals.- 19.1 Mean-Filter and Gaussian-Filter.- 19.2 Median-Filter.- 19.3 Smoothed Median-Filter.- 19.4 Edge Preserving Smoothing.- 19.5 K-Nearest Neighbor Averaging.- 19.6 Conditional Average Filter.- 19.7 Linear Reconstruction.- 19.8 Elimination of Noisy Image Rows.- 19.9 Resolution Hierarchies.- 20 Histogram Algorithms.- 20.1 Discriminant and Least Squares Threshold.- 20.2 Histogram Entropy Thresholding.- 20.3 Multithresholding.- 20.4 Local Histogram Equalization.- 20.5 Lookup Table Transformation.- 20.6 A Class for Histograms.- 21 Edge Images.- 21.1 Robert’s Cross.- 21.2 Second Derivative.- 21.3 Edge Model Masks.- 21.4 Alternative Methods.- 21.5 Thinning of Edge Images.- 21.6 Edge Thresholding.- 21.7 Non Maxima Suppression.- 21.8 Non Maxima Absorption.- 21.9 Class Edge Revisited.- 22 Line Detection Algorithms.- 22.1 Line Detection.- 22.2 Local Connectivity.- 22.3 Hysteresis Thresholds.- 22.4 Closing of Gaps.- 22.5 Zero crossings in Laplace-Images.- 22.6 Hough Transform.- 22.7 Canny Line Detection.- 22.8 Shen and Castan.- 22.9 Representation as Segmentation Objects.- 23 Chain Codes.- 23.1 Length of a Chain.- 23.2 Smoothing.- 23.3 Digital Linear Lines.- 23.4 Similarity.- 23.5 Intersections.- 23.6 Rotation.- 23.7 Conversion.- IV Appendix.- A Basics of C++.- A.1 History.- A.2 Identifier and Constants.- A.3 Basis Data Types in C and C++.- B Software Development Tools.- B.1 Groups and ID’s with Unix.- B.2 Program Building with make.- B.3 The Use of Libraries.- B.4 Version and Access Control with rcs.- C Source Codes and Tools.- C.1 List of Tools.- C.2 How to get the sources.- C.3 X11.- C.4 Slides.- C.5 Addresses.- C.6 Headers and Source Files.- C.7 Dummy Definitions.- C.7.1 Listing of animals/dummy/Dictionary.h.- C.7.2 Listing of animals/dummy/OIOxdr.h.- C.7.3 Listing of animals/ dummy/Represent.h.- C.7.4 Listing of animals/dummy/SeqCltn.h.- C.7.5 Listing of animals/dummy/Set.h.- C.7.6 Listing of animals/dummy/dummies. C.- References.- Figures.- Tables.- Index for Exercises.