Image Processing

TINKU ACHARYA, PhD, is Chief Technology Officer, Avisere, Inc., Tucson, Arizona. Dr. Acharya is also Adjunct Professor, Department of Electrical Engineering, Arizona State University. He is the inventor of ninety-seven awarded U.S. and European patents and coauthor of Data Mining: Multimedia, Soft Computing, and Bioinformatics (Wiley), Information Technology, and JPEG2000 Standard for Image Compression: Concepts, Algorithms, and VLSI Architectures (Wiley).AJOY K. RAY, PhD, is Senior Corporate Research Scientist, Avisere, Inc., Arizona, and a Professor of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur. A distinguished teacher with twenty-five years of experience, Professor Ray has coauthored two other books, Advanced Microprocessors and Peripherals and Information Technology: Principles and Applications. In addition, he has published extensively in international journals.

"The avowed aim of this book is to describe both modern and older techniques in image processing in an introductory textbook. This is a very real need, and this book is certainly useful for this purpose…" (Computing Reviews.com, December 20, 2005) "…suitable for use as a text in a formal setting, or for the individual…" (Books-On-Line.com)

• Preface1. Introduction1.1 Fundamentals of Image Processing1.2 Applications of Image Processing1.3 Human Visual Perception1.4 Components of an Image Processing System1.5 Organization of this book.1.6 How is this book different?1.7 Summary2. Image Formation and Representation2.1 Introduction2.2 Image formation2.3 Sampling and Quantization2.4 Binary Image2.5 Connected component labeling2.6 Image fled formats2.7 Some Important Notes2.8 Types of Image Processing Operations2.9 Summary3. Color and Color Imagery3.1 Introduction3.2 Perception of Colors and Spectral sensitivity of human eyes3.3 Color Space Quantization and the Just Noticeable Difference (JND)3.4 Color Space and Transformation3.5 Color Interpolation or Demosaicing3.6 Summary4. Image Transformation4.1 Introduction4.2 Fourier Transforms4.3 Discrete Cosine Transform4.4 Walsh Hadamard Transform (WHT)4.5 Karhaunen-Loeve Transform or Principal Component Analysis4.6 Summary5. Discrete Wavelet Transform5.1 Introduction5.2 Wavelet Transforms5.3 Extension to Two-Dimensional Signals5.4 Lifting Implementation of the DWT5.5 Why Do We Care About Lifting?5.6 Applications Areas in Image Processing5.7 Summary6. Image Enhancement and Restoration.6.1 Introduction6.2 Distinction between image enhancement and restoration6.3 Spatial Image Enhancement Techniques6.4 Noise Filtering6.5 Image Enhancement - Frequency Domain approach6.6 Noise Modeling6.7 Image Restoration6.8 Summary7. Image Segmentation7.1 Preliminaries7.2 Edge, Line, and Point Detection7.3 Edge Detector7.4 Image Thresholding Techniques7.5 Color Image Segmentation7.6 Waterfall algorithm for segmentation7.7 Document Image segmentation7.8 Summary8. Recognition of Image Patterns8.1 Introduction8.2 Bayesian Decision Theory8.3 Non-parametric Classification8.4 Unsupervised Classification Strategies - clustering8.5 K-means Clustering Algorithm8.6 Primitive selection Strategies8.7 High Dimensional Pattern Grammars8.8 Formal Linguistic model8.9 Automata Theory8.10 Structural recognition of imprecise Patterns8.11 Symbolic Projection Method8.12 Classification using Neural Networks8.13 Crisp Neural Networks For Scene Classification8.14 Architecture of Back propagation network8.15 Research Direction8.16 Summary9. Texture and Shape Analysis9.1 Introduction9.2 Drawbacks of Grey Level Co-occurrence Matrix (GLCM)9.3 Spatial Relationship9.4 Weak Texture Measures9.5 Strong Texture Measures and Generalized Co-occurrence9.6 Texture Spectrum9.7 Texture Classification using Fractals9.8 Fractals in Texture Classification9.9 Structural Methods9.10 Shape Analysis9.11 Dominant points in Shape Description9.12 Polygonal Approximation for Shape Analysis9.13 Automatic recognition of Guns9.14 Active Contour modeling9.15 Gestalt Theory of Perception9.16 Summary10. Fuzzy Set Theory in Image Processing10.1 Introduction to Fuzzy Set Theory10.2 Why Fuzzy Image?10.3 Introduction to Fuzzy Set Theory10.4 Preliminaries and Background10.5 Image as a Fuzzy Set10.6 Fuzzy Methods of Contrast Enhancement10.7 Determination of the Fuzzication Parameters10.8 Results10.9 Fuzzy Spatial Filter for Noise Removal10.10 Smoothing Algorithm10.11 Fuzzy Histogram Modeling10.12 Image Segmentation using Fuzzy Methods10.13 Fuzzy C Means Algorithm10.14 Fuzzy Approaches to Pattern Recognition10.15 Fusion of fuzzy logic with neural networks10.16 Summary11. Image Mining and Content Based Image Retrieval11.1 Introduction11.2 Representation of images in a CBIR System11.3 Model of a image retrieval system11.4 Image Mining11.5 Video Mining11.6 Summary12. Biometric And Biomedical Image Processing12.1 Introduction12.2 Face Recognition12.3 Face Recognition Using Eigenfaces.12.4 Signature Verification12.5 Preprocessing of Signature Patterns12.6 Biomedical Image Analysis12.7 X - ray Image Analysis12.8 Uses of X-ray images12.9 Biomedical Imaging Techniques12.10 Dental x-ray image analysis12.11 Mammogram Image Analysis12.12 Research direction12.13 Summary13. Remotely Sensed Multispectral Scene Analysis13.1 Introduction13.2 Satellite sensors and imageries13.3 Features of Multispectral Images13.4 Spectral reflectance of various earth objects13.5 Scene Classification Strategies13.6 Spectral classification - A knowledge Based Approach13.7 Spatial Reasoning13.8 Fuzzy Set Theoretic Approaches in Remote Sensing13.9 Summary14. Dynamic Scene Analysis: Moving Object Detection and Tracking14.1 Introduction14.2 Problem Definition14.3 Adaptive Background Modelling14.4 Connected Component Labeling14.5 Shadow Detection14.6 Principles of object Tracking14.7 Model of Tracker System14.8 Condensation Algorithm14.9 Particle Filter Based object Tracking14.10 Summary15. Introduction to Image Compression15.1 Introduction15.2 Information Theory Concepts15.3 Classification of Compression algorithms15.4 Source Coding Algorithms15.5 Huffman Coding15.6 Arithmetic Coding15.7 Summary16. JPEG: Still Image Compression Standard16.1 Introduction16.2 The JPEG Lossless Coding Algorithm16.3 Baseline JPEG Compression16.4 Summary17. JPEG2000 Standard17.1 Introduction17.2 Why JPEG2000?17.3 Parts of the JPEG2000 Standard17.4 Overview of the JPEG2000 Part 1 Encoding System17.5 Image Preprocessing17.6 Compression17.7 Tier-2 Coding and Bitstream Formation17.8 Summary18. Coding Algorithms in JPEG200018.1 Introduction18.2 Partitioning Data for Coding18.3 Tier-1 Coding in JPEG200018.4 Tier-2 Coding in JPEG200018.5 SummaryReferencesIndexAbout the Authors