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Handbook of Computer Vision Algorithms in Image Algebra 2nd edition [Kõva köide]

(University of Florida, Gainesville, USA),
  • Formaat: Hardback, 444 pages, kõrgus x laius: 254x178 mm, kaal: 979 g, 33 Tables, black and white
  • Ilmumisaeg: 21-Sep-2000
  • Kirjastus: CRC Press Inc
  • ISBN-10: 0849300754
  • ISBN-13: 9780849300752
Teised raamatud teemal:
  • Formaat: Hardback, 444 pages, kõrgus x laius: 254x178 mm, kaal: 979 g, 33 Tables, black and white
  • Ilmumisaeg: 21-Sep-2000
  • Kirjastus: CRC Press Inc
  • ISBN-10: 0849300754
  • ISBN-13: 9780849300752
Teised raamatud teemal:
This textbook surveys techniques commonly used in computer vision algorithm development and introduces the portable iaC++ library. The second edition adds a chapter on geometric image transformations. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Image algebra is a comprehensive, unifying theory of image transformations, image analysis, and image understanding. In 1996, the bestselling first edition of the Handbook of Computer Vision Algorithms in Image Algebra introduced engineers, scientists, and students to this powerful tool, its basic concepts, and its use in the concise representation of computer vision algorithms.

Updated to reflect recent developments and advances, the second edition continues to provide an outstanding introduction to image algebra. It describes more than 80 fundamental computer vision techniques and introduces the portable iaC++ library, which supports image algebra programming in the C++ language. Revisions to the first edition include a new chapter on geometric manipulation and spatial transformation, several additional algorithms, and the addition of exercises to each chapter.

The authors-both instrumental in the groundbreaking development of image algebra-introduce each technique with a brief discussion of its purpose and methodology, then provide its precise mathematical formulation. In addition to furnishing the simple yet powerful utility of image algebra, the Handbook of Computer Vision Algorithms in Image Algebra supplies the core of knowledge all computer vision practitioners need. It offers a more practical, less esoteric presentation than those found in research publications that will soon earn it a prime location on your reference shelf.

Arvustused

"Every person who uses computer image processing...who is planning to use image processing...who is involved in the purchase of computer systems or software that involve image processing...should read this book. And if you are not in one of the above groups, you might want to read it anyway." - Microscopy Research and Technique

Image Algebra
1(55)
Introduction
1(3)
Point Sets
4(6)
Value Sets
10(3)
Images
13(10)
Templates
23(10)
Recursive Templates
33(4)
Neighborhoods
37(5)
The p-Product
42(5)
Exercise
47(3)
References
50(5)
Image Enhancement Techniques
55(30)
Introduction
55(1)
Averaging of Multiple Images
55(2)
Local Averaging
57(1)
Variable Local Averaging
57(1)
Iterative Conditional Local Averaging
58(1)
Gaussian Smoothing
59(1)
Max-Min Sharpening Transform
60(2)
Smoothing Binary Images by Association
62(3)
Median Filter
65(3)
Unsharp Masking
68(2)
Local Area Contrast Enhancement
70(1)
Histogram Equalization
71(1)
Histogram Modification
72(1)
Lowpass Filtering
73(8)
Highpass Filtering
81(1)
Exercises
82(2)
References
84(1)
Edge Detection and Boundary Finding Techniques
85(52)
Introduction
85(1)
Binary Image Boundaries
85(2)
Edge Enhancement by Discrete Differencing
87(3)
Roberts Edge Detector
90(1)
Prewitt Edge Detector
91(2)
Sobel Edge Detector
93(1)
Wallis Logarithmic Edge Detection
94(2)
Frei-Chen Edge and Line Detection
96(3)
Kirsch Edge Detector
99(2)
Directional Edge Detection
101(2)
Product of the Difference of Averages
103(2)
Canny Edge Detection
105(4)
Crack Edge Detection
109(2)
Marr-Hildreth Edge Detection
111(3)
Local Edge Detection in Three-Dimensional Images
114(2)
Hierarchical Edge Detection
116(2)
Edge Detection Using K-Forms
118(4)
Hueckel Edge Operator
122(6)
Divide-and-Conquer Boundary Detection
128(3)
Edge Following as Dynamic Programming
131(3)
Exercises
134(1)
References
135(2)
Thresholding Techniques
137(18)
Introduction
137(1)
Global Thresholding
137(1)
Semithresholding
138(2)
Multilevel Thresholding
140(1)
Variable Thresholding
141(1)
Threshold Selection Using Mean and Standard Deviation
141(2)
Threshold Selection by Maximizing Between-Class Variance
143(6)
Threshold Selection Using a Simple Image Statistic
149(4)
Exercises
153(1)
References
153(2)
Thinning and Skeletonizing
155(18)
Introduction
155(1)
Pavlidis Thinning Algorithm
155(2)
Medial Axis Transform (MAT)
157(2)
Distance Transforms
159(4)
Zhang-Suen Skeletonizing
163(3)
Zhang-Suen Transform -- Modified to Preserve Homotopy
166(2)
Thinning Edge Magnitude Images
168(3)
Exercises
171(1)
References
171(2)
Connected Component Algorithms
173(14)
Introduction
173(1)
Component Labeling for Binary Images
173(3)
Labeling Components with Sequential Labels
176(2)
Counting Connected Components by Shrinking
178(3)
Pruning of Connected Components
181(1)
Hole Filling
182(1)
Exercises
183(2)
References
185(2)
Morphological Transforms and Techniques
187(18)
Introduction
187(1)
Basic Morphological Operations: Boolean Dilations and Erosions
187(5)
Opening and Closing
192(1)
Salt and Pepper Noise Removal
193(2)
The Hit-and-Miss Transform
195(2)
Gray Value Dilations, Erosions, Openings, and Closings
197(2)
The Rolling Ball Algorithm
199(2)
Exercises
201(1)
References
202(3)
Linear Image Transforms
205(38)
Introduction
205(1)
Fourier Transform
205(3)
Centering the Fourier Transform
208(3)
Fast Fourier Transform
211(6)
Discrete Cosine Transform
217(4)
Walsh Transform
221(4)
The Haar Wavelet Transform
225(8)
Daubechies Wavelet Transforms
233(6)
Exercises
239(1)
References
240(3)
Pattern Matching and Shape Detection
243(32)
Introduction
243(1)
Pattern Matching Using Correlation
243(4)
Pattern Matching in the Frequency Domain
247(5)
Rotation Invariant Pattern Matching
252(3)
Rotation and Scale Invariant Pattern Matching
255(2)
Line Detection Using the Hough Transform
257(7)
Detecting Ellipses Using the Hough Transform
264(5)
Generalized Hough Algorithm for Shape Detection
269(3)
Exercises
272(1)
References
273(2)
Image Features and Descriptors
275(34)
Introduction
275(1)
Area and Perimeter
275(1)
Euler Number
276(2)
Chain Code Extraction and Correlation
278(5)
Region Adjacency
283(3)
Inclusion Relation
286(3)
Quadtree Extraction
289(3)
Position, Orientation, and Symmetry
292(2)
Region Description Using Moments
294(2)
Histogram
296(2)
Cumulative Histogram
298(1)
Texture Descriptors: Spatial Gray Level Dependence Statistics
299(6)
Exercises
305(1)
References
306(3)
Geometric Image Transformations
309(24)
Introduction
309(1)
Image Reflection and Magnification
309(2)
Nearest Neighbor Image Rotation
311(2)
Image Rotation using Bilinear Interpolation
313(3)
Application of Image Rotation to the Computation of Directional Edge Templates
316(4)
General Affine Transforms
320(2)
Fractal Constructs
322(5)
Iterated Function Systems
327(2)
Exercises
329(1)
References
330(3)
Neural Networks and Cellular Automata
333(34)
Introduction
333(1)
Hopfield Neural Network
334(6)
Bidirectional Associative Memory (BAM)
340(5)
Hamming Net
345(4)
Single-Layer Perceptron (SLP)
349(3)
Multilayer Perceptron (MLP)
352(7)
Cellular Automata and Life
359(1)
Solving Mazes Using Cellular Automata
360(2)
Exercises
362(2)
References
364(3)
Appendix. The Image Algebra C++ Library 367(46)
Index 413
Joseph N. Wilson, Gerhard X. Ritter