Verfügbarkeit: Digital image processing using Matlab

Digital image processing using Matlab:

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Bibliographische Detailangaben
Beteiligte Personen:	Gonzalez, Rafael C. (VerfasserIn), Woods, Richard E. (VerfasserIn), Eddins, Steven L. (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Upper Saddle River, NJ Pearson Prentice Hall 2004
Schlagwörter:	Bildverarbeitung - MATLAB MATLAB Bildverarbeitung
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010765703&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	XIV, 609 S. Ill., graph. Darst.
ISBN:	0130085197

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adam_text	DIGITAL IMAGE PROCESSING USING MATLAB 9 RAFAEL C. GONZALEZ UNIVERSITY OF TENNESSEE RICHARD E. WOODS MEDDATA INTERACTIVE STEVEN L. EDDINS THE MATHWORKS, INC. PEARSON PRENTICE HALL UPPER SADDLE RIVER, NJ 07458 CONTENTS PREFACE XI ACKNOWLEDGMENTS XII ABOUT THE AUTHORS XIII INTRODUCTION 1 PREVIEW 1 1 BACKGROUND 1 2 WHAT IS DIGITAL IMAGE PROCESSING? 2 3 BACKGROUND ON MATLAB AND THE IMAGE PROCESSING TOOLBOX I AREAS OF IMAGE PROCESSING COVERED IN THE BOOK 5 5 THE BOOK WEB SITE 6 I NOTATION 7 F THE MATLAB WORKING ENVIRONMENT 7 1.7.1 THE MATLAB DESKTOP 7 1.7.2 USING THE MATLAB EDITOR TO CREATE M-FILES 9 1.7.3 GETTING HELP 9 1.7.4 SAVING AND RETRIEVING A WORK SESSION 10 HOW REFERENCES ARE ORGANIZED IN THE BOOK 11 SUMMARY 11 FUNDAMENTALS 12 PREVIEW 12 DIGITAL IMAGE REPRESENTATION 12 2.1.1 COORDINATE CONVENTIONS 13 2.1.2 IMAGES AS MATRICES 14 READING IMAGES 14 DISPLAYING IMAGES 16 WRITING IMAGES 18 DATA CLASSES 23 IMAGE TYPES 24 2.6.1 INTENSITY IMAGES 24 2.6.2 BINARY IMAGES 25 2.6.3 A NOTE ON TERMINOLOGY 25 CONVERTING BETWEEN DATA CLASSES AND IMAGE TYPES 25 2.7.1 CONVERTING BETWEEN DATA CLASSES 25 2.7.2 CONVERTING BETWEEN IMAGE CLASSES AND TYPES 26 ARRAY INDEXING 30 2.8.1 VECTOR INDEXING 30 2.8.2 MATRIX INDEXING 32 2.8.3 SELECTING ARRAY DIMENSIONS 37 VI * CONTENTS 2.9 SOME IMPORTANT STANDARD ARRAYS 37 2.10 INTRODUCTION TO M-FUNCTION PROGRAMMING 38 2.10.1 M-FILES 38 2.10.2 OPERATORS 40 2.10.3 FLOW CONTROL 49 2.10.4 CODE OPTIMIZATION 55 2.10.5 INTERACTIVE I/O 59 2.10.6 A BRIEF INTRODUCTION TO CELL ARRAYS AND STRUCTURES 62 SUMMARY 64 I INTENSITY TRANSFORMATIONS AND SPATIAL FILTERING 65 PREVIEW 65 3.1 BACKGROUND 65 3.2 INTENSITY TRANSFORMATION FUNCTIONS 66 3.2.1 FUNCTION IMADJUST 66 3.2.2 LOGARITHMIC AND CONTRAST-STRETCHING TRANSFORMATIONS 68 3.2.3 SOME UTILITY M-FUNCTIONS FOR INTENSITY TRANSFORMATIONS 70 3.3 HISTOGRAM PROCESSING AND FUNCTION PLOTTING 76 3.3.1 GENERATING AND PLOTTING IMAGE HISTOGRAMS 76 3.3.2 HISTOGRAM EQUALIZATION 81 3.3.3 HISTOGRAM MATCHING (SPECIFICATION) 84 3.4 SPATIAL FILTERING 89 3.4.1 LINEAR SPATIAL FILTERING 89 3.4.2 NONLINEAR SPATIAL FILTERING 96 3.5 IMAGE PROCESSING TOOLBOX STANDARD SPATIAL FILTERS 99 3.5.1 LINEAR SPATIAL FILTERS 99 3.5.2 NONLINEAR SPATIAL FILTERS 104 SUMMARY 107 I FREQUENCY DOMAIN PROCESSING 108 PREVIEW 108 4.1 THE 2-D DISCRETE FOURIER TRANSFORM 108 4.2 COMPUTING AND VISUALIZING THE 2-D DFT IN MATLAB 112 4.3 FILTERING IN THE FREQUENCY DOMAIN 115 4.3.1 FUNDAMENTAL CONCEPTS 115 4.3.2 BASIC STEPS IN DFT FILTERING 121 4.3.3 AN M-FUNCTION FOR FILTERING IN THE FREQUENCY DOMAIN 122 4.4 OBTAINING FREQUENCY DOMAIN FILTERS FROM SPATIAL FILTERS 122 4.5 GENERATING FILTERS DIRECTLY IN THE FREQUENCY DOMAIN 127 4.5.1 CREATING MESHGRID ARRAYS FOR USE IN IMPLEMENTING FILTERS IN THE FREQUENCY DOMAIN 128 4.5.2 LOWPASS FREQUENCY DOMAIN FILTERS 129 4.5.3 WIREFRAME AND SURFACE PLOTTING 132 * CONTENTS VII SHARPENING FREQUENCY DOMAIN FILTERS 136 4.6.1 BASIC HIGHPASS FILTERING 136 4.6.2 HIGH-FREQUENCY EMPHASIS FILTERING 138 SUMMARY 140 IMAGE RESTORATION 141 PREVIEW 141 A MODEL OF THE IMAGE DEGRADATION/RESTORATION PROCESS 142 NOISE MODELS 143 5.2.1 ADDING NOISE WITH FUNCTION IMNOISE 143 5.2.2 GENERATING SPATIAL RANDOM NOISE WITH A SPECIFIED DISTRIBUTION 144 5.2.3 PERIODIC NOISE 150 5.2.4 ESTIMATING NOISE PARAMETERS 153 RESTORATION IN THE PRESENCE OF NOISE ONLYSPATIAL FILTERING 158 5.3.1 SPATIAL NOISE FILTERS 159 5.3.2 ADAPTIVE SPATIAL FILTERS 164 PERIODIC NOISE REDUCTION BY FREQUENCY DOMAIN FILTERING 166 '. MODELING THE DEGRADATION FUNCTION 166 ' T DIRECT INVERSE FILTERING 169 \| WIENER FILTERING 170 I CONSTRAINED LEAST SQUARES (REGULARIZED) FILTERING 173 FLTERATIVE NONLINEAR RESTORATION USING THE LUCY-RICHARDSON FALGORITHM 176 [BLIND DECONVOLUTION 179 {GEOMETRIC TRANSFORMATIONS AND IMAGE REGISTRATION 182 15.11.1 GEOMETRIC SPATIAL TRANSFORMATIONS 182 5.11.2 APPLYING SPATIAL TRANSFORMATIONS TO IMAGES 187 5.11.3 IMAGE REGISTRATION 191 SUMMARY 193 LOLOR IMAGE PROCESSING 194 MEW 194 DIOR IMAGE REPRESENTATION IN MATLAB 194 1.1.1 RGB IMAGES 194 1.1.2 INDEXED IMAGES 197 {,1.3 IPT FUNCTIONS FOR MANIPULATING RGB AND INDEXED IMAGES 199 DNVERTING TO OTHER COLOR SPACES 204 2.1 NTSC COLOR SPACE 204 THE YCBCR COLOR SPACE 205 THE HSV COLOR SPACE 205 L4 THE CMY AND CMYK COLOR SPACES 206 2.5 THE HSI COLOR SPACE 207 TIE BASICS OF COLOR IMAGE PROCESSING 215 DIOR TRANSFORMATIONS 216 VIII * CONTENTS 6.5 SPATIAL FILTERING OF COLOR IMAGES 227 6.5.1 COLOR IMAGE SMOOTHING 227 6.5.2 COLOR IMAGE SHARPENING 230 6.6 WORKING DIRECTLY IN RGB VECTOR SPACE 231 6.6.1 COLOR EDGE DETECTION USING THE GRADIENT 232 6.6.2 IMAGE SEGMENTATION IN RGB VECTOR SPACE 237 SUMMARY 241 / WAVELETS 242 PREVIEW 242 7.1 BACKGROUND 242 7.2 THE FAST WAVELET TRANSFORM 245 7.2.1 FWTS USING THE WAVELET TOOLBOX 246 7.2.2 FWTS WITHOUT THE WAVELET TOOLBOX 252 7.3 WORKING WITH WAVELET DECOMPOSITION STRUCTURES 259 7.3.1 EDITING WAVELET DECOMPOSITION COEFFICIENTS WITHOUT THE WAVELET TOOLBOX 262 7.3.2 DISPLAYING WAVELET DECOMPOSITION COEFFICIENTS 266 7.4 THE INVERSE FAST WAVELET TRANSFORM 271 7.5 WAVELETS IN IMAGE PROCESSING 276 SUMMARY 281 8 IMAGE CONTPRESSION 282 PREVIEW 282 8.1 BACKGROUND 283 8.2 CODING REDUNDANCY 286 8.2.1 HUFFMAN CODES 289 8.2.2 HUFFMAN ENCODING 295 8.2.3 HUFFMAN DECODING 301 8.3 INTERPIXEL REDUNDANCY 309 8.4 PSYCHOVISUAL REDUNDANCY 315 8.5 JPEG COMPRESSION 317 8.5.1 JPEG 318 8.5.2 JPEG 2000 325 SUMMARY 333 / MORPHOLOGICAL IMAGE PROCESSING 334 PREVIEW 334 9.1 PRELIMINARIES 335 9.1.1 SOME BASIC CONCEPTS FROM SET THEORY 335 9.1.2 BINARY IMAGES, SETS, AND LOGICAL OPERATORS 337 9.2 DILATION AND EROSION 337 9.2.1 DILATION 338 9.2.2 STRUCTURING ELEMENT DECOMPOSITION 341 9.2.3 THE ST RE 1 FUNCTION 341 9.2.4 EROSION 345 CONTENTS IX COMBINING DILATION AND EROSION 347 9.3.1 OPENING AND CLOSING 347 9.3.2 THE HIT-OR-MISS TRANSFORMATION 350 9.3.3 USING LOOKUP TABLES 353 9.3.4 FUNCTION BWMORPH 356 LABELING CONNECTED COMPONENTS 359 MORPHOLOGICAL RECONSTRUCTION 362 9.5.1 OPENING BY RECONSTRUCTION 363 9.5.2 FILLING HOLES 365 9.5.3 CLEARING BORDER OBJECTS 366 GRAY-SCALE MORPHOLOGY 366 9.6.1 DILATION AND EROSION 366 9.6.2 OPENING AND CLOSING 369 9.6.3 RECONSTRUCTION 374 SUMMARY 377 IMAGE SEGMENTATION 3 78 PREVIEW 378 POINT, LINE, AND EDGE DETECTION 379 10.1.1 POINT DETECTION 379 10.1.2 LINE DETECTION 381 10.1.3 EDGE DETECTION USING FUNCTION EDGE 384 LINE DETECTION USING THE HOUGH TRANSFORM 393 10.2.1 HOUGH TRANSFORM PEAK DETECTION 399 10.2.2 HOUGH TRANSFORM LINE DETECTION AND LINKING 401 THRESHOLDING 404 10.3.1 GLOBAL THRESHOLDING 405 10.3.2 LOCAL THRESHOLDING 407 \|4 REGION-BASED SEGMENTATION 407 10.4.1 BASIC FORMULATION 407 10.4.2 REGION GROWING 408 10.4.3 REGION SPLITTING AND MERGING 412 SEGMENTATION USING THE WATERSHED TRANSFORM 417 10.5.1 WATERSHED SEGMENTATION USING THE DISTANCE TRANSFORM 10.5.2 WATERSHED SEGMENTATION USING GRADIENTS 420 10.5.3 MARKER-CONTROLLED WATERSHED SEGMENTATION 422 SUMMARY 425 REPRESENTATION AND DESCRIPTION 426 PREVIEW 426 BACKGROUND 426 11.1.1 CELL ARRAYS AND STRUCTURES 427 11.1.2 SOME ADDITIONAL MATLAB AND IPT FUNCTIONS USED IN THIS CHAPTER 432 11.1.3 SOME BASIC UTILITY M-FUNCTIONS 433 418 * CONTENTS 11.2 REPRESENTATION 436 11.2.1 CHAIN CODES 436 11.2.2 POLYGONAL APPROXIMATIONS USING MINIMUM-PERIMETER POLYGONS 439 11.2.3 SIGNATURES 449 11.2.4 BOUNDARY SEGMENTS 452 11.2.5 SKELETONS 453 11.3 BOUNDARY DESCRIPTORS 455 11.3.1 SOME SIMPLE DESCRIPTORS 455 11.3.2 SHAPE NUMBERS 456 11.3.3 FOURIER DESCRIPTORS 458 11.3.4 STATISTICAL MOMENTS 462 11.4 REGIONAL DESCRIPTORS 463 11.4.1 FUNCTION REGIONPROPS 463 11.4.2 TEXTURE 464 11.4.3 MOMENT INVARIANTS 470 11.5 USING PRINCIPAL COMPONENTS FOR DESCRIPTION 474 SUMMARY 483 I / OBJECT RECOGNITION 484 PREVIEW 484 12.1 BACKGROUND 484 12.2 COMPUTING DISTANCE MEASURES IN MATLAB 485 12.3 RECOGNITION BASED ON DECISION-THEORETIC METHODS 488 12.3.1 FORMING PATTERN VECTORS 488 12.3.2 PATTERN MATCHING USING MINIMUM-DISTANCE CLASSIFIERS 489 12.3.3 MATCHING BY CORRELATION 490 12.3.4 OPTIMUM STATISTICAL CLASSIFIERS 492 12.3.5 ADAPTIVE LEARNING SYSTEMS 498 12.4 STRUCTURAL RECOGNITION 498 12.4.1 WORKING WITH STRINGS IN MATLAB 499 12.4.2 STRING MATCHING 508 SUMMARY 513 A FUNCON SUMMARY SU I X D ICE AND MATLAB GRAPHICAL USER INTERFACES 527 LIX I M-FUNCTIONS 552 BIBLIOGRAPHY 594 INDEX 597
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title	Digital image processing using Matlab
title_auth	Digital image processing using Matlab
title_exact_search	Digital image processing using Matlab
title_full	Digital image processing using Matlab Rafael C. Gonzalez ; Richard E. Woods ; Steven L. Eddins
title_fullStr	Digital image processing using Matlab Rafael C. Gonzalez ; Richard E. Woods ; Steven L. Eddins
title_full_unstemmed	Digital image processing using Matlab Rafael C. Gonzalez ; Richard E. Woods ; Steven L. Eddins
title_short	Digital image processing using Matlab
title_sort	digital image processing using matlab
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