This comprehensive and state-of-the art approach to image processing gives engineers and students a thorough introduction, and includes full coverage of key applications: image watermarking, fingerprint recognition, face recognition and iris recognition and medical imaging. "This book combines basic image processing techniques with some of the most advanced procedures. Introductory chapters dedicated to general principles are presented alongside detailed application-orientated ones. As a result it is suitably adapted for different classes of readers, ranging from Master to PhD students and beyond." - Prof. Jean-Philippe Thiran, EPFL, Lausanne, Switzerland
"Al Bovik’s compendium proceeds systematically from fundamentals to today’s research frontiers. Professor Bovik, himself a highly respected leader in the field, has invited an all-star team of contributors. Students, researchers, and practitioners of image processing alike should benefit from the Essential Guide." - Prof. Bernd Girod, Stanford University, USA
"This book is informative, easy to read with plenty of examples, and allows great flexibility in tailoring a course on image processing or analysis." - Prof. Pamela Cosman, University of California, San Diego, USA
1. Chapter 1. Introduction to Digital Image Processing 2. 1.1. TYPES OF IMAGES 3. 1.2. SCALE OF IMAGES 4. 1.3. DIMENSION OF IMAGES 5. 1.4. DIGITIZATION OF IMAGES 6. 1.5. SAMPLED IMAGES 7. 1.6. QUANTIZED IMAGES 8. 1.7. COLOR IMAGES 9. 1.8. SIZE OF IMAGE DATA 10. 1.9. OBJECTIVES OF THIS GUIDE 11. 1.10. ORGANIZATION OF THE GUIDE 12. Chapter 2. The SIVA Image Processing Demos 13. 2.1. INTRODUCTION 14. 2.2. LabVIEW FOR IMAGE PROCESSING 15. 2.3. EXAMPLES FROM THE SIVA IMAGE PROCESSING DEMOS 16. 2.4. CONCLUSIONS 17. Chapter 3. Basic Gray Level Image Processing 18. 3.1. INTRODUCTION 19. 3.2. NOTATION 20. 3.3. IMAGE HISTOGRAM 21. 3.4. LINEAR POINT OPERATIONS ON IMAGES 22. 3.5. NONLINEAR POINT OPERATIONS ON IMAGES 23. 3.6. ARITHMETIC OPERATIONS BETWEEN IMAGES 24. 3.7. GEOMETRIC IMAGE OPERATIONS 25. Chapter 4. Basic Binary Image Processing 26. 4.1. INTRODUCTION 27. 4.2. IMAGE THRESHOLDING 28. 4.3. REGION LABELING 29. 4.4. BINARY IMAGE MORPHOLOGY 30. 4.5. BINARY IMAGE REPRESENTATION AND COMPRESSION 31. Chapter 5. Basic Tools for Image Fourier Analysis 32. 5.1. INTRODUCTION 33. 5.2. DISCRETE-SPACE SINUSOIDS 34. 5.3. DISCRETE-SPACE FOURIER TRANSFORM 35. 5.4. 2D DISCRETE FOURIER TRANSFORM (DFT) 36. 5.5. UNDERSTANDING IMAGE FREQUENCIES AND THE DFT 37. 5.6. RELATED TOPICS IN THIS GUIDE 38. Chapter 6. Multiscale Image Decompositions and Wavelets 39. 6.1. OVERVIEW 40. 6.2. PYRAMID REPRESENTATIONS 41. 6.3. WAVELET REPRESENTATIONS 42. 6.4. OTHER MULTISCALE DECOMPOSITIONS 43. 6.5. CONCLUSION 44. Chapter 7. Image Noise Models 45. 7.1. SUMMARY 46. 7.2. PRELIMINARIES 47. 7.3. ELEMENTS OF ESTIMATION THEORY 48. 7.4. TYPES OF NOISE AND WHERE THEY MIGHT OCCUR 49. 7.5. CCD IMAGING 50. 7.6. SPECKLE 51. 7.7. CONCLUSIONS 52. Chapter 8. Color and Multispectral Image Representation and Display 53. 8.1. INTRODUCTION 54. 8.2. PRELIMINARY NOTES ON DISPLAY OF IMAGES 55. 8.3. NOTATION AND PREREQUISITE KNOWLEDGE 56. 8.4. ANALOG IMAGES AS PHYSICAL FUNCTIONS 57. 8.5. COLORIMETRY 58. 8.6. SAMPLING OF COLOR SIGNALS AND SENSORS 59. 8.7. COLOR I/O DEVICE CALIBRATION 60. 8.8. SUMMARY AND FUTURE OUTLOOK 61. Chapter 9. Capturing Visual Image Properties with Probabilistic Models 62. 9.1. THE GAUSSIAN MODEL 63. 9.2. THE WAVELET MARGINAL MODEL 64. 9.3. WAVELET LOCAL CONTEXTUAL MODELS 65. 9.4. DISCUSSION 66. Chapter 10. Basic Linear Filtering with Application to Image Enhancement 67. 10.1. INTRODUCTION 68. 10.2. IMPULSE RESPONSE, LINEAR CONVOLUTION, AND FREQUENCY RESPONSE 69. 10.3. LINEAR IMAGE ENHANCEMENT 70. 10.4. DISCUSSION 71. Chapter 11. Multiscale Denoising of Photographic Images 72. 11.1. INTRODUCTION 73. 11.2. DISTINGUISHING IMAGES FROM NOISE IN MULTISCALE REPRESENTATIONS 74. 11.3. SUBBAND DENOISING—A GLOBAL APPROACH 75. 11.4. SUBBAND COEFFICIENT DENOISING—A POINTWISE APPROACH 76. 11.5. SUBBAND NEIGHBORHOOD DENOISING—STRIKING A BALANCE 77. 11.6. STATISTICAL MODELING FOR OPTIMAL DENOISING 78. 11.7. CONCLUSIONS 79. Chapter 12. Nonlinear Filtering for Image Analysis and Enhancement 80. 12.1. INTRODUCTION 81. 12.2. WEIGHTED MEDIAN SMOOTHERS AND FILTERS 82. 12.3. IMAGE NOISE CLEANING 83. 12.4. IMAGE ZOOMING 84. 12.5. IMAGE SHARPENING 85. 12.6. CONCLUSION 86. Chapter 13. Morphological Filtering 87. 13.1. INTRODUCTION 88. 13.2. MORPHOLOGICAL IMAGE OPERATORS 89. 13.3. MORPHOLOGICAL FILTERS FOR IMAGE ENHANCEMENT 90. 13.4. MORPHOLOGICAL OPERATORS FOR TEMPLATE MATCHING 91. 13.5. MORPHOLOGICAL OPERATORS FOR FEATURE DETECTION 92. 13.6. DESIGN APPROACHES FOR MORPHOLOGICAL FILTERS 93. 13.7. CONCLUSIONS 94. Chapter 14. Basic Methods for Image Restoration and Identification 95. 14.1. INTRODUCTION 96. 14.2. BLUR MODELS 97. 14.3. IMAGE RESTORATION ALGORITHMS 98. 14.4. BLUR IDENTIFICATION ALGORITHMS 99. Chapter 15. Iterative Image Restoration 100. 15.1. INTRODUCTION 101. 15.2. ITERATIVE RECOVERY ALGORITHMS 102. 15.3. SPATIALLY INVARIANT DEGRADATION 103. 15.4. MATRIX-VECTOR FORMULATION 104. 15.5. USE OF CONSTRAINTS 105. 15.6. ADDITIONAL CONSIDERATIONS 106. 15.7. DISCUSSION 107. Chapter 16. Lossless Image Compression 108. 16.1. INTRODUCTION 109. 16.2. BASICS OF LOSSLESS IMAGE CODING 110. 16.3. LOSSLESS SYMBOL CODING 111. 16.4. LOSSLESS CODING STANDARDS 112. 16.5. OTHER DEVELOPMENTS IN LOSSLESS CODING 113. Chapter 17. JPEG and JPEG2000 114. 17.1. INTRODUCTION 115. 17.2. LOSSY JPEG CODEC STRUCTURE 116. 17.3. DISCRETE COSINE TRANSFORM 117. 17.4. QUANTIZATION 118. 17.5. COEFFICIENT-TO-SYMBOL MAPPING AND CODING 119. 17.6. IMAGE DATA FORMAT AND COMPONENTS 120. 17.7. ALTERNATIVE MODES OF OPERATION 121. 17.8. JPEG PART 3 122. 17.9. THE JPEG2000 STANDARD 123. 17.10. JPEG2000 PART 1: CODING ARCHITECTURE 124. 17.11. PERFORMANCE AND EXTENSIONS 125. 17.12. ADDITIONAL INFORMATION 126. Chapter 18. Wavelet Image Compression 127. 18.1. WHAT ARE WAVELETS: WHY ARE THEY GOOD FOR IMAGE CODING? 128. 18.2. THE COMPRESSION PROBLEM 129. 18.3. THE TRANSFORM CODING PARADIGM 130. 18.4. SUBBAND CODING: THE EARLY DAYS 131. 18.5. NEW AND MORE EFFICIENT CLASS OF WAVELET CODERS 132. 18.6. ADAPTIVE WAVELET TRANSFORMS: WAVELET PACKETS 133. 18.7. JPEG2000 AND RELATED DEVELOPMENTS 134. 18.8. CONCLUSION 135. Chapter 19. Gradient and Laplacian Edge Detection 136. 19.1. INTRODUCTION 137. 19.2. Gradient-Based Methods 138. 19.3. LAPLACIAN-BASED METHODS 139. 19.4. CANNY'S METHOD 140. 19.5. APPROACHES FOR COLOR AND MULTISPECTRAL IMAGES 141. 19.6. SUMMARY 142. Chapter 20. Diffusion Partial Differential Equations for Edge Detection 143. 20.1. INTRODUCTION AND MOTIVATION 144. 20.2. BACKGROUND ON DIFFUSION 145. 20.3. ANISOTROPIC DIFFUSION TECHNIQUES 146. 20.4. APPLICATION OF ANISOTROPIC DIFFUSION TO EDGE DETECTION 147. 20.5. USING VECTOR DIFFUSION AND PARAMETRIC ACTIVE CONTOURS TO LOCATE EDGES 148. 20.6. CONCLUSIONS 149. Chapter 21. Image Quality Assessment 150. 21.1. INTRODUCTION 151. 21.2. HUMAN VISION MODELING BASED METRICS 152. 21.3. STRUCTURAL APPROACHES 153. 21.4. INFORMATION THEORETIC APPROACHES 154. 21.5. PERFORMANCE OF IMAGE QUALITY METRICS 155. 21.6. CONCLUSION 156. Chapter 22. Image Watermarking 157. 22.1. INTRODUCTION 158. 22.2. APPLICATIONS OF WATERMARKING TECHNIQUES 159. 22.3. CLASSIFICATION OF WATERMARKING ALGORITHMS 160. 22.4. WATERMARK EMBEDDING, DETECTION, AND DECODING 161. 22.5. COPYRIGHT PROTECTION WATERMARKING 162. 22.6. IMAGE CONTENT INTEGRITY AND AUTHENTICATION WATERMARKING 163. Chapter 23. Fingerprint Recognition 164. 23.1. INTRODUCTION 165. 23.2. EMERGING APPLICATIONS 166. 23.3. FINGERPRINT AS A BIOMETRIC 167. 23.4. HISTORY OF FINGERPRINTS 168. 23.5. SYSTEM ARCHITECTURE 169. 23.6. FINGERPRINT SENSING 170. 23.7. FINGERPRINT FEATURES 171. 23.8. FEATURE EXTRACTION 172. 23.9. FINGERPRINT ENHANCEMENT 173. 23.10. FINGERPRINT CLASSIFICATION 174. 23.11. FINGERPRINT MATCHING 175. 23.12. SUMMARY AND FUTURE PROSPECTS 176. Chapter 24. Unconstrained Face Recognition from a Single Image 177. 24.1. INTRODUCTION 178. 24.2. LINEAR LAMBERTIAN OBJECT: FACE RECOGNITION UNDER ILLUMINATION VARIATION 179. 24.3. ILLUMINATING LIGHT FIELD: FACE RECOGNITION UNDER ILLUMINATION AND POSE VARIATIONS 180. 24.4. FACE MODELING AND VERIFICATION ACROSS AGE PROGRESSION 181. 24.5. CONCLUSIONS 182. Chapter 25. How Iris Recognition Works 183. 25.1. INTRODUCTION 184. 25.2. LOCALIZING THE IRIS AND ITS BOUNDARIES 185. 25.3. IRIS FEATURE ENCODING BY 2D GABOR WAVELET DEMODULATION 186. 25.4. THE TEST OF STATISTICAL INDEPENDENCE: COMBINATORICS OF PHASE SEQUENCES 187. 25.5. RECOGNIZING IRISES REGARDLESS OF SIZE, POSITION, AND ORIENTATION 188. 25.6. UNIQUENESS OF FAILING THE TEST OF STATISTICAL INDEPENDENCE 189. 25.7. DECISION ENVIRONMENT FOR IRIS RECOGNITION 190. 25.8. SPEED PERFORMANCE SUMMARY 191. 25.9. APPENDIX: 2D FOCUS ASSESSMENT AT THE VIDEO FRAME RATE 192. Chapter 26. Computed Tomography 193. 26.1. INTRODUCTION 194. 26.2. BACKGROUND 195. 26.3. 2D IMAGE RECONSTRUCTION 196. 26.4. EXTENDING 2D METHODS INTO 3D 197. 26.5. 3D IMAGE RECONSTRUCTION 198. 26.6. ITERATIVE RECONSTRUCTION METHODS 199. 26.7. SUMMARY 200. Chapter 27. Computer-Assisted Microscopy 201. 27.1. INTRODUCTION 202. 27.2. COMPUTER-ASSISTED MICROSCOPY SYSTEMS 203. 27.3. SOFTWARE FOR HARDWARE CONTROL 204. 27.4. IMAGE PROCESSING AND ANALYSIS SOFTWARE 205. 27.5. A COMPUTERIZED MICROSCOPY SYSTEM FOR CLINICAL CYTOGENETICS 206. 27.6. APPLICATIONS IN CLINICAL CYTOGENETICS 207. 27.7. COMMERCIALLY AVAILABLE SYSTEMS 208. 27.8. CONCLUSIONS 209. Chapter 28. Towards Video Processing 210. Index
This comprehensive and state-of-the art approach to image processing gives engineers and students a thorough introduction, and includes full coverage of key applications: image watermarking, fingerprint recognition, face recognition and iris recognition and medical imaging. "This book combines basic image processing techniques with some of the most advanced procedures. Introductory chapters dedicated to general principles are presented alongside detailed application-orientated ones. As a result it is suitably adapted for different classes of readers, ranging from Master to PhD students and beyond." - Prof. Jean-Philippe Thiran, EPFL, Lausanne, Switzerland
"Al Bovik’s compendium proceeds systematically from fundamentals to today’s research frontiers. Professor Bovik, himself a highly respected leader in the field, has invited an all-star team of contributors. Students, researchers, and practitioners of image processing alike should benefit from the Essential Guide." - Prof. Bernd Girod, Stanford University, USA
"This book is informative, easy to read with plenty of examples, and allows great flexibility in tailoring a course on image processing or analysis." - Prof. Pamela Cosman, University of California, San Diego, USA
1. Chapter 1. Introduction to Digital Image Processing 2. 1.1. TYPES OF IMAGES 3. 1.2. SCALE OF IMAGES 4. 1.3. DIMENSION OF IMAGES 5. 1.4. DIGITIZATION OF IMAGES 6. 1.5. SAMPLED IMAGES 7. 1.6. QUANTIZED IMAGES 8. 1.7. COLOR IMAGES 9. 1.8. SIZE OF IMAGE DATA 10. 1.9. OBJECTIVES OF THIS GUIDE 11. 1.10. ORGANIZATION OF THE GUIDE 12. Chapter 2. The SIVA Image Processing Demos 13. 2.1. INTRODUCTION 14. 2.2. LabVIEW FOR IMAGE PROCESSING 15. 2.3. EXAMPLES FROM THE SIVA IMAGE PROCESSING DEMOS 16. 2.4. CONCLUSIONS 17. Chapter 3. Basic Gray Level Image Processing 18. 3.1. INTRODUCTION 19. 3.2. NOTATION 20. 3.3. IMAGE HISTOGRAM 21. 3.4. LINEAR POINT OPERATIONS ON IMAGES 22. 3.5. NONLINEAR POINT OPERATIONS ON IMAGES 23. 3.6. ARITHMETIC OPERATIONS BETWEEN IMAGES 24. 3.7. GEOMETRIC IMAGE OPERATIONS 25. Chapter 4. Basic Binary Image Processing 26. 4.1. INTRODUCTION 27. 4.2. IMAGE THRESHOLDING 28. 4.3. REGION LABELING 29. 4.4. BINARY IMAGE MORPHOLOGY 30. 4.5. BINARY IMAGE REPRESENTATION AND COMPRESSION 31. Chapter 5. Basic Tools for Image Fourier Analysis 32. 5.1. INTRODUCTION 33. 5.2. DISCRETE-SPACE SINUSOIDS 34. 5.3. DISCRETE-SPACE FOURIER TRANSFORM 35. 5.4. 2D DISCRETE FOURIER TRANSFORM (DFT) 36. 5.5. UNDERSTANDING IMAGE FREQUENCIES AND THE DFT 37. 5.6. RELATED TOPICS IN THIS GUIDE 38. Chapter 6. Multiscale Image Decompositions and Wavelets 39. 6.1. OVERVIEW 40. 6.2. PYRAMID REPRESENTATIONS 41. 6.3. WAVELET REPRESENTATIONS 42. 6.4. OTHER MULTISCALE DECOMPOSITIONS 43. 6.5. CONCLUSION 44. Chapter 7. Image Noise Models 45. 7.1. SUMMARY 46. 7.2. PRELIMINARIES 47. 7.3. ELEMENTS OF ESTIMATION THEORY 48. 7.4. TYPES OF NOISE AND WHERE THEY MIGHT OCCUR 49. 7.5. CCD IMAGING 50. 7.6. SPECKLE 51. 7.7. CONCLUSIONS 52. Chapter 8. Color and Multispectral Image Representation and Display 53. 8.1. INTRODUCTION 54. 8.2. PRELIMINARY NOTES ON DISPLAY OF IMAGES 55. 8.3. NOTATION AND PREREQUISITE KNOWLEDGE 56. 8.4. ANALOG IMAGES AS PHYSICAL FUNCTIONS 57. 8.5. COLORIMETRY 58. 8.6. SAMPLING OF COLOR SIGNALS AND SENSORS 59. 8.7. COLOR I/O DEVICE CALIBRATION 60. 8.8. SUMMARY AND FUTURE OUTLOOK 61. Chapter 9. Capturing Visual Image Properties with Probabilistic Models 62. 9.1. THE GAUSSIAN MODEL 63. 9.2. THE WAVELET MARGINAL MODEL 64. 9.3. WAVELET LOCAL CONTEXTUAL MODELS 65. 9.4. DISCUSSION 66. Chapter 10. Basic Linear Filtering with Application to Image Enhancement 67. 10.1. INTRODUCTION 68. 10.2. IMPULSE RESPONSE, LINEAR CONVOLUTION, AND FREQUENCY RESPONSE 69. 10.3. LINEAR IMAGE ENHANCEMENT 70. 10.4. DISCUSSION 71. Chapter 11. Multiscale Denoising of Photographic Images 72. 11.1. INTRODUCTION 73. 11.2. DISTINGUISHING IMAGES FROM NOISE IN MULTISCALE REPRESENTATIONS 74. 11.3. SUBBAND DENOISING—A GLOBAL APPROACH 75. 11.4. SUBBAND COEFFICIENT DENOISING—A POINTWISE APPROACH 76. 11.5. SUBBAND NEIGHBORHOOD DENOISING—STRIKING A BALANCE 77. 11.6. STATISTICAL MODELING FOR OPTIMAL DENOISING 78. 11.7. CONCLUSIONS 79. Chapter 12. Nonlinear Filtering for Image Analysis and Enhancement 80. 12.1. INTRODUCTION 81. 12.2. WEIGHTED MEDIAN SMOOTHERS AND FILTERS 82. 12.3. IMAGE NOISE CLEANING 83. 12.4. IMAGE ZOOMING 84. 12.5. IMAGE SHARPENING 85. 12.6. CONCLUSION 86. Chapter 13. Morphological Filtering 87. 13.1. INTRODUCTION 88. 13.2. MORPHOLOGICAL IMAGE OPERATORS 89. 13.3. MORPHOLOGICAL FILTERS FOR IMAGE ENHANCEMENT 90. 13.4. MORPHOLOGICAL OPERATORS FOR TEMPLATE MATCHING 91. 13.5. MORPHOLOGICAL OPERATORS FOR FEATURE DETECTION 92. 13.6. DESIGN APPROACHES FOR MORPHOLOGICAL FILTERS 93. 13.7. CONCLUSIONS 94. Chapter 14. Basic Methods for Image Restoration and Identification 95. 14.1. INTRODUCTION 96. 14.2. BLUR MODELS 97. 14.3. IMAGE RESTORATION ALGORITHMS 98. 14.4. BLUR IDENTIFICATION ALGORITHMS 99. Chapter 15. Iterative Image Restoration 100. 15.1. INTRODUCTION 101. 15.2. ITERATIVE RECOVERY ALGORITHMS 102. 15.3. SPATIALLY INVARIANT DEGRADATION 103. 15.4. MATRIX-VECTOR FORMULATION 104. 15.5. USE OF CONSTRAINTS 105. 15.6. ADDITIONAL CONSIDERATIONS 106. 15.7. DISCUSSION 107. Chapter 16. Lossless Image Compression 108. 16.1. INTRODUCTION 109. 16.2. BASICS OF LOSSLESS IMAGE CODING 110. 16.3. LOSSLESS SYMBOL CODING 111. 16.4. LOSSLESS CODING STANDARDS 112. 16.5. OTHER DEVELOPMENTS IN LOSSLESS CODING 113. Chapter 17. JPEG and JPEG2000 114. 17.1. INTRODUCTION 115. 17.2. LOSSY JPEG CODEC STRUCTURE 116. 17.3. DISCRETE COSINE TRANSFORM 117. 17.4. QUANTIZATION 118. 17.5. COEFFICIENT-TO-SYMBOL MAPPING AND CODING 119. 17.6. IMAGE DATA FORMAT AND COMPONENTS 120. 17.7. ALTERNATIVE MODES OF OPERATION 121. 17.8. JPEG PART 3 122. 17.9. THE JPEG2000 STANDARD 123. 17.10. JPEG2000 PART 1: CODING ARCHITECTURE 124. 17.11. PERFORMANCE AND EXTENSIONS 125. 17.12. ADDITIONAL INFORMATION 126. Chapter 18. Wavelet Image Compression 127. 18.1. WHAT ARE WAVELETS: WHY ARE THEY GOOD FOR IMAGE CODING? 128. 18.2. THE COMPRESSION PROBLEM 129. 18.3. THE TRANSFORM CODING PARADIGM 130. 18.4. SUBBAND CODING: THE EARLY DAYS 131. 18.5. NEW AND MORE EFFICIENT CLASS OF WAVELET CODERS 132. 18.6. ADAPTIVE WAVELET TRANSFORMS: WAVELET PACKETS 133. 18.7. JPEG2000 AND RELATED DEVELOPMENTS 134. 18.8. CONCLUSION 135. Chapter 19. Gradient and Laplacian Edge Detection 136. 19.1. INTRODUCTION 137. 19.2. Gradient-Based Methods 138. 19.3. LAPLACIAN-BASED METHODS 139. 19.4. CANNY'S METHOD 140. 19.5. APPROACHES FOR COLOR AND MULTISPECTRAL IMAGES 141. 19.6. SUMMARY 142. Chapter 20. Diffusion Partial Differential Equations for Edge Detection 143. 20.1. INTRODUCTION AND MOTIVATION 144. 20.2. BACKGROUND ON DIFFUSION 145. 20.3. ANISOTROPIC DIFFUSION TECHNIQUES 146. 20.4. APPLICATION OF ANISOTROPIC DIFFUSION TO EDGE DETECTION 147. 20.5. USING VECTOR DIFFUSION AND PARAMETRIC ACTIVE CONTOURS TO LOCATE EDGES 148. 20.6. CONCLUSIONS 149. Chapter 21. Image Quality Assessment 150. 21.1. INTRODUCTION 151. 21.2. HUMAN VISION MODELING BASED METRICS 152. 21.3. STRUCTURAL APPROACHES 153. 21.4. INFORMATION THEORETIC APPROACHES 154. 21.5. PERFORMANCE OF IMAGE QUALITY METRICS 155. 21.6. CONCLUSION 156. Chapter 22. Image Watermarking 157. 22.1. INTRODUCTION 158. 22.2. APPLICATIONS OF WATERMARKING TECHNIQUES 159. 22.3. CLASSIFICATION OF WATERMARKING ALGORITHMS 160. 22.4. WATERMARK EMBEDDING, DETECTION, AND DECODING 161. 22.5. COPYRIGHT PROTECTION WATERMARKING 162. 22.6. IMAGE CONTENT INTEGRITY AND AUTHENTICATION WATERMARKING 163. Chapter 23. Fingerprint Recognition 164. 23.1. INTRODUCTION 165. 23.2. EMERGING APPLICATIONS 166. 23.3. FINGERPRINT AS A BIOMETRIC 167. 23.4. HISTORY OF FINGERPRINTS 168. 23.5. SYSTEM ARCHITECTURE 169. 23.6. FINGERPRINT SENSING 170. 23.7. FINGERPRINT FEATURES 171. 23.8. FEATURE EXTRACTION 172. 23.9. FINGERPRINT ENHANCEMENT 173. 23.10. FINGERPRINT CLASSIFICATION 174. 23.11. FINGERPRINT MATCHING 175. 23.12. SUMMARY AND FUTURE PROSPECTS 176. Chapter 24. Unconstrained Face Recognition from a Single Image 177. 24.1. INTRODUCTION 178. 24.2. LINEAR LAMBERTIAN OBJECT: FACE RECOGNITION UNDER ILLUMINATION VARIATION 179. 24.3. ILLUMINATING LIGHT FIELD: FACE RECOGNITION UNDER ILLUMINATION AND POSE VARIATIONS 180. 24.4. FACE MODELING AND VERIFICATION ACROSS AGE PROGRESSION 181. 24.5. CONCLUSIONS 182. Chapter 25. How Iris Recognition Works 183. 25.1. INTRODUCTION 184. 25.2. LOCALIZING THE IRIS AND ITS BOUNDARIES 185. 25.3. IRIS FEATURE ENCODING BY 2D GABOR WAVELET DEMODULATION 186. 25.4. THE TEST OF STATISTICAL INDEPENDENCE: COMBINATORICS OF PHASE SEQUENCES 187. 25.5. RECOGNIZING IRISES REGARDLESS OF SIZE, POSITION, AND ORIENTATION 188. 25.6. UNIQUENESS OF FAILING THE TEST OF STATISTICAL INDEPENDENCE 189. 25.7. DECISION ENVIRONMENT FOR IRIS RECOGNITION 190. 25.8. SPEED PERFORMANCE SUMMARY 191. 25.9. APPENDIX: 2D FOCUS ASSESSMENT AT THE VIDEO FRAME RATE 192. Chapter 26. Computed Tomography 193. 26.1. INTRODUCTION 194. 26.2. BACKGROUND 195. 26.3. 2D IMAGE RECONSTRUCTION 196. 26.4. EXTENDING 2D METHODS INTO 3D 197. 26.5. 3D IMAGE RECONSTRUCTION 198. 26.6. ITERATIVE RECONSTRUCTION METHODS 199. 26.7. SUMMARY 200. Chapter 27. Computer-Assisted Microscopy 201. 27.1. INTRODUCTION 202. 27.2. COMPUTER-ASSISTED MICROSCOPY SYSTEMS 203. 27.3. SOFTWARE FOR HARDWARE CONTROL 204. 27.4. IMAGE PROCESSING AND ANALYSIS SOFTWARE 205. 27.5. A COMPUTERIZED MICROSCOPY SYSTEM FOR CLINICAL CYTOGENETICS 206. 27.6. APPLICATIONS IN CLINICAL CYTOGENETICS 207. 27.7. COMMERCIALLY AVAILABLE SYSTEMS 208. 27.8. CONCLUSIONS 209. Chapter 28. Towards Video Processing 210. Index
