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Image processing, analysis, and machine vision 1st ed

Image processing, analysis, and machine vision 1st ed (Loan 2 times)

Material type
단행본
Personal Author
Sonka, Milan. Hlavac, Vaclav. Boyle, Roger.
Title Statement
Image processing, analysis, and machine vision / Milan Sonka, Vaclav Hlavac, and Roger Boyle.
판사항
1st ed.
Publication, Distribution, etc
London ;   New York :   Chapman & Hall Computing ,   1993.  
Physical Medium
xix, 555 p. : ill. ; 24 cm.
Series Statement
Chapman & Hall computing series
ISBN
0412455706
Bibliography, Etc. Note
Includes bibliographical references (p. 534-542) and index.
Subject Added Entry-Topical Term
Image processing. Computer vision. Image analysis.
000 01111camuu2200337 a 4500
001 000045412233
005 20080121103635
008 940321s1993 enka b 001 0 eng d
010 ▼a 94133813 //r98
020 ▼a 0412455706
035 ▼a (KERIS)REF000005063343
040 ▼a EUE ▼c EUE ▼d AzU ▼d DLC ▼d 211009
042 ▼a lccopycat
050 0 0 ▼a TA1637 ▼b .S66 1993
082 0 0 ▼a 006.3/7 ▼2 22
090 ▼a 006.37 ▼b S698i
100 1 ▼a Sonka, Milan.
245 1 0 ▼a Image processing, analysis, and machine vision / ▼c Milan Sonka, Vaclav Hlavac, and Roger Boyle.
250 ▼a 1st ed.
260 ▼a London ; ▼a New York : ▼b Chapman & Hall Computing , ▼c 1993.
300 ▼a xix, 555 p. : ▼b ill. ; ▼c 24 cm.
490 1 ▼a Chapman & Hall computing series
504 ▼a Includes bibliographical references (p. 534-542) and index.
650 0 ▼a Image processing.
650 0 ▼a Computer vision.
650 0 ▼a Image analysis.
700 1 ▼a Hlavac, Vaclav.
700 1 ▼a Boyle, Roger.
830 0 ▼a Chapman and Hall computing series.
945 ▼a KINS

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.37 S698i Accession No. 121163053 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents


CONTENTS
List of Algorithms = xi
List of symbols and abbreviations = xiii
Preface = xv
1 Introduction = 1
2 The digitized image and its properties = 13
 2.1 Basic consepts = 13
  2.1.1 Image functions = 13
  2.1.2 The Dirac distribution and convolution = 16
  2.1.3 The Fourier transform = 17
  2.1.4 Images as a stochasfic process = 19
  2.1.5 Images as linear systems = 22
 2.2 Image digitization = 22
  2.2.1 Sampling = 23
  2.2.2 Quantization = 28
  2.2.3 Colour images = 28
 2.3 Digital image properties = 30
  2.3.1 Metric and Topological properties of digital images = 30
  2.3.2 Histograms = 35
  2.3.3 Visual perception of the image = 36
  2.3.4 Image quality = 39
  2.3.5 Noise in images = 39
3 Data structures for image analysis = 42
 3.1 levels of image data representation = 42
 3.2 Traditional image data structures = 43
  3.2.1 Matrices = 44
  3.2.2 Chains = 46
  3.2.3 Topological data structures = 47
  3.2.4 Relational structures = 49
 3.3 Hierarchical data structures = 50
  3.3.1 Pyramids = 50
  3.3.2 Quadtrees = 52
4 Image pre-processing = 56
 4.1 Pixel brightness transformations = 57
  4.1.1 Position-dependent brightness correction = 57
  4.1.2 Grey scale transformation = 58
 4.2 Geometric transformations = 61
  4.2.1 Pixel co-ordinate transformations = 62
  4.2.2 Brightness interpolation = 64
 4.3 Local pre-processing = 67
  4.3.1 Image smoothing = 68
  4.3.2 Edge detectors = 76
  4.3.3 Zero Crossings of the second derivative = 82
  4.3.4 Scale in image processing = 86
  4.3.5 Canny dege detection = 88
  4.3.6 Edges in multispectral images = 91
  4.3.7 Other local pre-processing operators = 92
  4.3.8 Adaptive neighbourhood pre-processing = 96
 4.4 Image restoration = 102
  4.4.1 Image restoration as inverse convolution of the whole image = 102
  4.4.2 Degradations that are easy to restore = 104
  4.4.3 Inverse filtration = 105
  4.4.4 Wiener filtration = 106
5 Segmentation = 112
 5.1 Thresholding = 113
  5.1.1 Threshold detection methods = 116
  5.1.2 Multispectral Thresholding = 120
  5.1.3 Thresholding in hierarchical data structures = 121
 5.2 Edge-based segmentation = 122
  5.2.1 Edge image Thresholding = 123
  5.2.2 Edge relaxation = 124
  5.2.3 Border tracing = 129
  5.2.4 Edge following as graph searching = 135
  5.2.5 Edge following as dynamic programming = 146
  5.2.6 Hough transforms = 149
  5.2.7 Border detection using border location information = 159
  5.2.8 Region construction from borders = 161
 5.3 Region growing segmentation = 164
  5.3.1 Region merging = 165
  5.3.2 Region splitting = 169
  5.3.3 Splitting and merging = 170
 5.4 Matching = 176
  5.4.1 Matching criteria = 176
  5.4.2 Control strategies of matching = 178
6 Shape representation and description = 192
 6.1 Region identification = 197
 6.2 Contour-based shape representation and description = 200
  6.2.1 Chain codes = 200
  6.2.2 Simple geometric border repredentation = 201
  6.2.3 Fourier transforms of boundaries = 205
  6.2.4 Boundary description using a segment sequence ; polygonal representation = 208
  6.2.5 B-spline representation = 212
  6.2.6 Other contour-based shape description approaches = 215
  6.2.7 Shape invariants = 215
 6.3 Region-based shape representation and descrption = 220
  6.3.1 Simple scalar region descriptors = 222
  6.3.2 Moments = 228
  6.3.3 Convex hull = 230
  6.3.4 Graph representation vased on region skeleton = 235
  6.3.5 Region decomposition = 240
  6.3.6 Region neighbourhood graphs = 241
7 Object recognition = 255
 7.1 Knowledge representation = 256
 7.2 statistical pattern recognition = 262
  7.2.1 Classification principles = 264
  7.2.2 Classifier setting = 266
  7.2.3 Classifier learning = 270
  7.2.4 Cluster analysis = 273
 7.3 Neural nets = 275
  7.3.1 Feed-forward nets = 276
  7.3.2 Kohonen feature maps = 279
  7.3.3 Hybrid neural nets = 280
  7.3.4 Hopfield neural nets = 280
 7.4 Syntactic pattern recognition = 283
  7.4.1 Grammars and languages = 285
  7.4.2 Syntactic analysis, syntactic classifier = 288
  7.4.3 Syntactic classifier learning, grammar inference = 290
 7.5 Recognition as graph matching = 292
  7.5.1 Isomorphism of graphs and subgraphs = 293
  7.5.2 Similarity of graphs = 298
 7.6 Optimization techniques in recognotion = 299
  7.6.1 Genetic algorithms = 301
  7.6.2 Simulated annealing = 304
8 Image understanding = 316
 8.1 Image understanding control strategies = 318
  8.1.1 Parallel and serial processing control = 318
  8.1.2 Hierarchical control = 319
  8.1.3 Bottom-up control strategier = 319
  8.1.4 Model-based control strategies = 320
  8.1.5 Combined control strategies = 322
  8.1.6 Non-Hierarchical control = 326
 8.2 Active contour models-snakes = 329
 8.3 Pattern recognition methods in image understanding = 335
  8.3.1 Contextual image classification = 336
 8.4 Scene labelling and constraint propagation = 343
  8.4.1 Discrete relaxation = 344
  8.4.2 Probabilistic relaxation = 347
  8.4.3 Searching interpretation and understanding = 350
 8.5 Semantic image segmentation and understanding = 351
  8.5.1 Semantic region growing = 353
  8.5.2 Semantic genetic segmentation and interpretation = 355
9 3D Vision = 373
 9.1 Strategy = 375
  9.1.1 Marr's theory = 375
  9.1.2 Modelling strategies = 378
 9.2 Line labelling = 382
 9.3 Shape from X = 385
  9.3.1 Shape from stereo = 385
  9.3.2 Shape from shading = 392
  9.3.3 Shape from motion = 396
  9.3.4 Shape from texture = 403
 9.4 Approaches to the recognition of 3D objects = 405
  9.4.1 Goad's algorithm = 406
  9.4.2 Features for model-based recognition of surved objects = 412
 9.5 Depth map technologies = 413
 9.6 Summary = 415
10 Mathematical morphology = 422
 10.1 basic primciples and morphological transformations = 423
  10.1.1 Morphological transformations = 423
  10.1.2 Dilation = 426
  10.1.3 Erosion = 428
  10.1.4 Opening and closing = 431
 10.2 Skeleton and other topological processing = 432
  10.2.1 Homotopic transformations = 432
  10.2.2 Skeleton = 433
  10.2.3 Thining and thickening = 434
  10.2.4 Conditional dilation and ultimate erosion = 439
11 Linear discrete image transforms = 443
 11.1 Basic theory = 444
 11.2 The Fourier transform = 445
 11.3 Hadamard transform = 447
 11.4 Discrete cosine transform = 448
 11.5 Other discrete image transform = 449
 11.6 Applications of discrete image transforms = 450
12 Image data compression = 458
 12.1 Image data propertire = 460
 12.2 Discrete image transforms in image data compression = 461
 12.3 predictive compression methods = 463
 12.4 Vector quantization = 465
 12.5 Pyramid compression methods = 466
 12.6 Comparison of compression methods = 468
 12.7 Other techniques = 470
13 Texture = 477
 13.1 Statictical texture descroption = 480
  13.1.1 Methods based on spatial frequencies = 480
  13.1.2 Co-occurrence matrices = 482
  13.1.3 Edge frequency = 485
  13.1.4 Primitive length (Run length) = 487
  13.1.5 Other statistical methods of texture description = 488
 13.2 Syntactic texture description methods = 490
  13.2.1 Shape chain grammars = 491
  13.2.2 Graph grammars = 493
  13.2.3 Primitive grouping in hierarchical textures = 494
 13.3 Hybrid tecture description methods = 497
 13.4 Texture recognition method applications = 498
14 Motion analysis = 507
 14.1 Differential motion analysis methods = 510
 14.2 Optical flow conputation = 512
  14.2.1 Optical flow conputation = 513
  14.2.2 Global and local optical flow estimation = 516
  14.2.3 Optical flow in motion analysis = 521
 14.3 Motion analysis based on detection of interest points = 524
  14.3.1 Detection of interest points = 525
  14.3.2 Correspondence of interest points = 525
Index = 543


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