Muutke küpsiste eelistusi

E-raamat: Image Processing and Mathematical Morphology: Fundamentals and Applications

3.00/5 (4 hinnangut Goodreads-ist)
(New Jersey Institute of Technology, Newark, USA)
  • Formaat: 439 pages
  • Ilmumisaeg: 12-Jul-2017
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781420089448
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 123,23 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Image Processing and Mathematical Morphology: Fundamentals and ApplicationsSuurem pilt
  • Formaat: 439 pages
  • Ilmumisaeg: 12-Jul-2017
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781420089448
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

In the development of digital multimedia, the importance and impact of image processing and mathematical morphology are well documented in areas ranging from automated vision detection and inspection to object recognition, image analysis and pattern recognition. Those working in these ever-evolving fields require a solid grasp of basic fundamentals, theory, and related applicationsand few books can provide the unique tools for learning contained in this text.

Image Processing and Mathematical Morphology: Fundamentals and Applications is a comprehensive, wide-ranging overview of morphological mechanisms and techniques and their relation to image processing. More than merely a tutorial on vital technical information, the book places this knowledge into a theoretical framework. This helps readers analyze key principles and architectures and then use the authors novel ideas on implementation of advanced algorithms to formulate a practical and detailed plan to develop and foster their own ideas. The book:











Presents the history and state-of-the-art techniques related to image morphological processing, with numerous practical examples





Gives readers a clear tutorial on complex technology and other tools that rely on their intuition for a clear understanding of the subject





Includes an updated bibliography and useful graphs and illustrations





Examines several new algorithms in great detail so that readers can adapt them to derive their own solution approaches

This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
Preface xv
Acknowledgments xix
Author xxi
1 Introduction to Mathematical Morphology 1
1.1 Basic Concept in Digital Image Processing
2
1.2 Brief History of Mathematical Morphology
5
1.3 Essential Morphological Approach to Image Analysis
5
1.4 Scope of This Book
7
References
7
Appendix: Selected List of Books on Image Processing and Mathematical Morphology
9
2 Binary Morphology 11
2.1 Set Operations on Binary Images
11
2.2 Logical Operations on Binary Images
13
2.3 Binary Dilation
15
2.3.1 Properties of Dilation
17
2.4 Binary Erosion
17
2.4.1 Properties of Erosion
18
2.5 Opening and Closing
20
2.5.1 Properties of Opening and Closing
21
2.6 Hit-or-Miss Transformation
22
References
24
3 Grayscale Morphology 25
3.1 Grayscale Dilation and Erosion
26
3.2 Grayscale Dilation Erosion Duality Theorem
32
3.3 Grayscale Opening and Closing
33
References
35
4 Basic Morphological Algorithms 37
4.1 Boundary Extraction
37
4.2 Region Filling
39
4.3 Extraction of Connected Components
39
4.4 Convex Hull
41
4.5 Thinning
43
4.6 Thickening
45
4.7 Skeletonization
45
4.8 Pruning
47
4.9 Morphological Edge Operator
48
4.9.1 Simple Morphological Edge Operators
48
4.9.2 Blur-Minimum Morphological Edge Operators
51
References
52
5 Basic Morphological Filters 55
5.1 Alternating Sequential Filters
56
5.1.1 Morphological Adjunction
57
5.1.2 Redundancy Removal in ASFs
58
5.1.3 Definitions of the New Class of ASFs
59
5.1.4 Properties of the New Class of ASFs
61
5.2 Recursive Morphological Filters
67
5.3 Soft Morphological Filters
72
5.3.1 Properties of Soft Morphological Operations
77
5.3.2 Idempotent Soft Morphological Filters
78
5.4 OSSM Filters
82
5.4.1 One-Dimensional Filtering Analysis
84
5.4.2 Two-Dimensional Filtering Analysis
85
5.4.3 Relationship between the OSSM Dilation and Erosion
86
5.4.4 Properties of OSSM Filters and Relationship to Other Nonlinear Filters
86
5.4.5 Experimental Results
90
5.4.6 Extensive Applications
92
5.5 RSM Filters (RSMFs)
97
5.5.1 Properties of RSMFs
103
5.5.2 Idempotent RSMFs
105
5.5.3 Cascaded RSMFs
106
5.6 ROSSM Filters
107
5.7 Regulated Morphological Filters
111
5.8 Fuzzy Morphological Filters
116
References
123
6 Distance Transformation 127
6.1 DT by Iterative Operations
128
6.2 DT by Mathematical Morphology
133
6.3 Approximation of Euclidean Distances
136
6.4 Decomposition of Distance SEs
139
6.4.1 Decomposition of City-Block and Chessboard Distance SEs
139
6.4.2 Decomposition of the Euclidean Distance Structuring Element
141
6.4.2.1 Construction Procedure
141
6.4.2.2 Computational Complexity
143
6.5 Iterative Erosion Algorithm
144
6.5.1 Redundant Calculations in the IEA
147
6.5.2 An Improved Iterative Erosion Algorithm
147
6.5.3 An Example of Improved Iterative Erosion Algorithm
150
6.6 Two Scan-Based Algorithm
152
6.6.1 Double Two-Scan Algorithm
152
6.6.2 Basic Ideas of Two-Scan Algorithms
157
6.6.3 Fundamental Lemmas
158
6.6.4 TS1—A Two Scan-Based EDT Algorithm for General Images
161
6.6.5 TSinfinity—A Two Scan-Based EDT Algorithm for Images with Obstacles
164
6.6.6 Computational Complexity
165
6.7 Three-Dimensional Euclidean Distance
168
6.7.1 Three-Dimensional Image Representation
168
6.7.2 Distance Definition Functions in the Three-Dimensional Domain
168
6.7.3 A Three-Dimensional Neighborhood in the EDT
169
6.8 Acquiring Approaches
170
6.8.1 Acquiring Approaches for City-Block and Chessboard DT
170
6.8.2 Acquiring Approaches for EDT
171
6.9 Deriving Approaches
173
6.9.1 Fundamental Lemmas
174
6.9.2 Two Scan-Based Algorithm for Three-Dimensional EDT
176
6.9.3 Complexity of the Two-Scan-Based Algorithm
179
References
179
7 Feature Extraction 183
7.1 Edge Linking by MM
183
7.1.1 Adaptive MM
184
7.1.2 Adaptive Morphological Edge-Linking Algorithm
185
7.1.3 Experimental Results
187
7.2 Corner Detection by Regulated Morphology
192
7.2.1 A Modified Laganiere's Operator
194
7.2.2 Modified Regulated Morphology for Corner Detection
195
7.2.3 Experimental Results
197
7.3 Shape Database with Hierarchical Features
199
7.3.1 Shape Number from DT
200
7.3.2 Significant Points Radius and Coordinates
202
7.3.3 Recognition by Matching Database
202
7.3.4 Localization by Hierarchical Morphological Band-Pass Filter
203
7.4 Corner and Circle Detection
204
7.5 Size Histogram
206
References
211
8 Object Representation 213
8.1 Object Representation and Tolerances
213
8.1.1 Representation Framework: Formal Languages and MM
214
8.1.2 Dimensional Attributes
215
8.1.2.1 The Two-Dimensional Attributes
215
8.1.2.2 The Three-Dimensional Attributes
216
8.1.2.3 Tolerancing Expression
218
8.2 Skeletonization or MA Transformation
219
8.2.1 Medial Axis Transformation by Morphological Dilations
221
8.2.2 Thick Skeleton Generation
222
8.2.2.1 The Skeleton from Distance Function
222
8.2.2.2 Detection of Ridge Points
223
8.2.2.3 Trivial Uphill Generation
223
8.2.3 Basic Definitions
224
8.2.3.1 Base Point
224
8.2.3.2 Apex Point
225
8.2.3.3 Directional-Uphill Generation
225
8.2.3.4 Directional-Downhill Generation
226
8.2.4 The Skeletonization Algorithm and Connectivity Properties
227
8.2.5 A Modified Algorithm
230
8.3 Morphological Shape Description
231
8.3.1 Introduction
231
8.3.2 G-Spectrum
233
8.3.3 The Properties of G-Spectrum
234
References
242
9 Decomposition of Morphological Structuring Elements 245
9.1 Decomposition of Geometric-Shaped SEs
245
9.1.1 Definitions of Types of SEs
246
9.1.2 Decomposition Properties
249
9.1.3 One-Dimensional Geometric-Shaped SEs
252
9.1.3.1 Semicircle, Semiellipse, Gaussian, Parabola, Semihyperbola, Cosine, and Sine
252
9.1.3.2 Decomposition Strategy
252
9.1.4 Two-Dimensional Geometric-Shaped SEs
257
9.1.4.1 Hemisphere, Hemiellipsoid, Gaussian, Elliptic Paraboloid, and Hemihyperboloid
257
9.1.4.2 Decomposition Strategy
258
9.1.5 Decomposition of a Large Transformed Cyclic Cosine Structuring Element
260
9.1.6 Decomposition of Two-Dimensional SEs into One-Dimensional Elements
263
9.2 Decomposition of Binary SEs
263
9.2.1 Overview of Decomposition Using GAs
264
9.2.2 Advantages of Structuring Element Decomposition
265
9.2.3 The Decomposition Technique Using GAs
266
9.2.4 Experimental Results
270
9.3 Decomposition of Grayscale SEs
272
9.3.1 General Properties of Structuring Element Decomposition
275
9.3.2 The One-Dimensional Arbitrary Grayscale Structuring Element Decomposition
275
9.3.3 The Two-Dimensional Arbitrary Grayscale Structuring Element Decomposition
280
9.3.4 Complexity Analysis
283
References
286
10 Architectures for Mathematical Morphology 289
10.1 Threshold Decomposition of Grayscale Morphology into Binary Morphology
290
10.1.1 Threshold Decomposition Algorithm for Grayscale Dilation
290
10.1.1.1 Notations and Definitions
291
10.1.1.2 Formulas' Derivation
293
10.1.1.3 Algorithm Description
295
10.1.1.4 Computational Complexity
298
10.1.2 A Simple Logic Implementation of Grayscale Morphological Operations
300
10.1.2.1 Binary Morphological Operations
300
10.1.2.2 Grayscale Morphological Operations
301
10.1.2.3 Additional Simplification
304
10.1.2.4 Implementation Complexity
304
10.2 Implementing Morphological Operations Using Programmable Neural Networks
306
10.2.1 Programmable Logic Neural Networks
308
10.2.2 Pyramid Neural Network Structure
311
10.2.3 Binary Morphological Operations by Logic Modules
312
10.2.4 Grayscale Morphological Operations by Tree Models
314
10.2.5 Improvement by Tri-Comparators
316
10.2.6 Another Improvement
317
10.2.7 Application of Morphological Operations on Neocognitron
320
10.3 MLP as Processing Modules
321
10.4 A Systolic Array Architecture
329
10.4.1 Basic Array Design
329
10.4.2 A Systolic Array for Processing One Scan of the Whole Image
332
10.4.3 The Complete Systolic Array for Processing Two Scans of the Whole Image
333
10.5 Implementation on Multicomputers
333
10.5.1 Characteristics of Multicomputers
334
10.5.2 Implementation of the Two-Scan Algorithm
335
10.5.3 Implementation of the Blocked Two-Scan Algorithm
336
10.5.4 Performance of Two-Scan Algorithms
337
References
338
11 General Sweep Mathematical Morphology 341
11.1 Introduction
342
11.2 Theoretical Development of General Sweep MM
343
11.2.1 Computation of Traditional Morphology
344
11.2.2 General Sweep MM
345
11.2.3 Properties of Sweep Morphological Operations
348
11.3 Blending of Sweep Surfaces with Deformations
350
11.4 Image Enhancement
352
11.5 Edge Linking
354
11.6 Geometric Modeling and Sweep MM
357
11.6.1 Tolerance Expression
360
11.6.2 Sweep Surface Modeling
361
11.7 Formal Language and Sweep Morphology
361
11.7.1 Two-Dimensional Attributes
363
11.7.2 Three-Dimensional Attributes
364
11.8 Grammars
366
11.8.1 Two-Dimensional Attributes
366
11.8.2 Three-Dimensional Attributes
369
11.9 Parsing Algorithms
370
References
373
12 Morphological Approach to Shortest Path Planning 377
12.1 Introduction
378
12.2 Relationships between Shortest Path Finding and MM
379
12.3 Rotational MM
380
12.3.1 Definitions
380
12.3.2 Properties
382
12.4 The Shortest Path—Finding Algorithm
383
12.4.1 Distance Transform
383
12.4.2 Describing the Algorithm
384
12.5 Experimental Results and Discussions
386
12.6 Dynamic Rotational MM
391
12.7 The Rule of Distance Functions in Shortest Path Planning
397
References
397
Index 399
New Jersey Institute of Technology, Newark, USA
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97814200894482e.html
Märksõnad: