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E-raamat: Introduction to Visual Computing: Core Concepts in Computer Vision, Graphics, and Image Processing

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(University of California, Irvine, USA), (University of California, Irvine, USA)
  • Formaat: 392 pages
  • Ilmumisaeg: 31-Jan-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781315355528
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Introduction to Visual Computing: Core Concepts in Computer Vision, Graphics, and Image ProcessingSuurem pilt
  • Formaat: 392 pages
  • Ilmumisaeg: 31-Jan-2018
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781315355528
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Introduction to Visual Computing: Core Concepts in Computer Vision, Graphics, and Image Processing covers the fundamental concepts of visual computing. Whereas past books have treated these concepts within the context of specific fields such as computer graphics, computer vision or image processing, this book offers a unified view of these core concepts, thereby providing a unified treatment of computational and mathematical methods for creating, capturing, analyzing and manipulating visual data (e.g. 2D images, 3D models). Fundamentals covered in the book include convolution, Fourier transform, filters, geometric transformations, epipolar geometry, 3D reconstruction, color and the image synthesis pipeline.

The book is organized in four parts. The first part provides an exposure to different kinds of visual data (e.g. 2D images, videos and 3D geometry) and the core mathematical techniques that are required for their processing (e.g. interpolation and linear regression.) The second part of the book on Image Based Visual Computing deals with several fundamental techniques to process 2D images (e.g. convolution, spectral analysis and feature detection) and corresponds to the low level retinal image processing that happens in the eye in the human visual system pathway.

The next part of the book on Geometric Visual Computing deals with the fundamental techniques used to combine the geometric information from multiple eyes creating a 3D interpretation of the object and world around us (e.g. transformations, projective and epipolar geometry, and 3D reconstruction). This corresponds to the higher level processing that happens in the brain combining information from both the eyes thereby helping us to navigate through the 3D world around us.

The last two parts of the book cover Radiometric Visual Computing and Visual Content Synthesis. These parts focus on the fundamental techniques for processing information arising from the interaction of light with objects around us, as well as the fundamentals of creating virtual computer generated worlds that mimic all the processing presented in the prior sections.

The book is written for a 16 week long semester course and can be used for both undergraduate and graduate teaching, as well as a reference for professionals.
Preface xi
I Fundamentals 1(32)
1 Data
3(18)
1.1 Visualization
4(1)
1.2 Discretization
4(4)
1.2.1 Sampling
5(2)
1.2.2 Quantization
7(1)
1.3 Representation
8(7)
1.3.1 Geometric Data
11(4)
1.4 Noise
15(1)
1.5 Conclusion
16(1)
Bibliography
17(1)
Summary
18(1)
Exercises
19(2)
2 Techniques
21(12)
2.1 Interpolation
21(5)
2.1.1 Linear Interpolation
22(1)
2.1.2 Bilinear Interpolation
23(3)
2.2 Geometric Intersections
26(3)
2.3 Conclusion
29(1)
Bibliography
29(1)
Summary
30(1)
Exercises
31(2)
II Image Based Visual Computing 33(92)
3 Convolution
35(32)
3.1 Linear Systems
35(6)
3.1.1 Response of a Linear System
36(4)
3.1.2 Properties of Convolution
40(1)
3.2 Linear Filters
41(19)
3.2.1 All, Low, Band and High Pass Filters
43(8)
3.2.2 Designing New Filters
51(3)
3.2.3 2D Filter Separability
54(2)
3.2.4 Correlation and Pattern Matching
56(4)
3.3 Implementation Details
60(1)
3.4 Conclusion
61(1)
Bibliography
62(1)
Summary
63(1)
Exercises
64(3)
4 Spectral Analysis
67(32)
4.1 Discrete Fourier Transform
67(7)
4.1.1 Why Sine and Cosine Waves?
73(1)
4.2 Polar Notation
74(6)
4.2.1 Properties
76(1)
4.2.2 Example Analysis of Signals
77(3)
4.3 Periodicity of Frequency Domain
80(1)
4.4 Aliasing
81(2)
4.5 Extension for 2D Interpretation
83(6)
4.5.1 Effect of Periodicity
85(1)
4.5.2 Notch Filter
85(2)
4.5.3 Example of Aliasing
87(2)
4.6 Duality
89(3)
4.7 Conclusion
92(1)
Bibliography
93(1)
Summary
94(1)
Exercises
95(4)
5 Feature Detection
99(26)
5.1 Edge Detection
100(13)
5.1.1 Edgel Detectors
100(11)
5.1.2 Multi-Resolution Edge Detection
111(2)
5.1.3 Aggregating Edgels
113(3)
5.2 Feature Detection
116(2)
5.3 Other Non-Linear Filters
118(2)
5.4 Conclusion
120(1)
Bibliography
120(1)
Summary
121(1)
Exercises
122(3)
III Geometric Visual Computing 125(74)
6 Geometric Transformations
127(30)
6.1 Homogeneous Coordinates
127(2)
6.2 Linear Transformations
129(2)
6.3 Euclidean and Affine Transformations
131(5)
6.3.1 Translation
131(1)
6.3.2 Rotation
132(2)
6.3.3 Scaling
134(1)
6.3.4 Shear
135(1)
6.3.5 Some Observations
136(1)
6.4 Concatenation of Transformations
136(5)
6.4.1 Scaling About the Center
137(1)
6.4.2 Rotation About an Arbitrary Axis
138(3)
6.5 Coordinate Systems
141(4)
6.5.1 Change of Coordinate Systems
142(3)
6.6 Properties of Concatenation
145(1)
6.6.1 Global vs Local Coordinate System
146(1)
6.7 Projective Transformation
146(2)
6.8 Degrees of Freedom
148(1)
6.9 Non-Linear Transformations
149(2)
6.10 Conclusion
151(1)
Bibliography
151(1)
Summary
152(1)
Exercises
153(4)
7 The Pinhole Camera
157(20)
7.1 The Model
157(11)
7.1.1 Camera Calibration
161(2)
7.1.2 3D Depth Estimation
163(1)
7.1.3 Homography
164(4)
7.2 Considerations in the Practical Camera
168(4)
7.3 Conclusion
172(1)
Bibliography
172(1)
Summary
173(1)
Exercises
174(3)
8 Epipolar Geometry
177(22)
8.1 Background
177(2)
8.2 Correspondences in Multi-View Geometry
179(3)
8.3 Fundamental Matrix
182(5)
8.3.1 Properties
183(1)
8.3.2 Estimating Fundamental Matrix
184(1)
8.3.3 Camera Setup Akin to Two Frontal Eyes
185(2)
8.4 Essential Matrix
187(1)
8.5 Rectification
187(3)
8.6 Applying Epipolar Geometry
190(4)
8.6.1 Depth from Disparity
190(2)
8.6.2 Depth from Optical Flow
192(2)
8.7 Conclusion
194(1)
Bibliography
195(1)
Summary
196(1)
Exercises
197(2)
IV Radiometric Visual Computing 199(78)
9 Light
201(22)
9.1 Radiometry
201(4)
9.1.1 Bidirectional Reflectance Distribution Function
203(2)
9.1.2 Light Transport Equation
205(1)
9.2 Photometry and Color
205(13)
9.2.1 CIE XYZ Color Space
208(3)
9.2.2 Perceptual Organization of CIE XYZ Space
211(5)
9.2.3 Perceptually Uniform Color Spaces
216(2)
9.3 Conclusion
218(1)
Bibliography
218(2)
Summary
220(1)
Exercises
221(2)
10 Color Reproduction
223(28)
10.1 Modeling Additive Color Mixtures
224(11)
10.1.1 Color Gamut of a Device
226(4)
10.1.2 Tone Mapping Operator
230(1)
10.1.3 Intensity Resolution
231(2)
10.1.4 Example Displays
233(2)
10.2 Color Management
235(3)
10.2.1 Gamut Transformation
236(1)
10.2.2 Gamut Matching
237(1)
10.3 Modeling Subtractive Color Mixture
238(2)
10.4 Limitations
240(5)
10.4.1 High Dynamic Range Imaging
240(3)
10.4.2 Multi-Spectral Imaging
243(2)
10.5 Conclusion
245(1)
Bibliography
246(2)
Summary
248(1)
Exercises
249(2)
11 Photometric Processing
251(26)
11.1 Histogram Processing
252(5)
11.1.1 Handling Color Images
255(2)
11.2 Image Composition
257(8)
11.2.1 Image Blending
257(6)
11.2.2 Image Cuts
263(2)
11.3 Photometric Stereo
265(7)
11.3.1 Handling Shadows
270(1)
11.3.2 Computing Illumination Directions
270(1)
11.3.3 Handling Color
271(1)
11.4 Conclusion
272(1)
Bibliography
272(2)
Summary
274(1)
Exercises
275(2)
V Visual Content Synthesis 277(92)
12 The Diverse Domain
279(12)
12.1 Modeling
279(2)
12.2 Processing
281(1)
12.3 Rendering
282(3)
12.4 Application
285(4)
12.5 Conclusion
289(1)
Bibliography
289(2)
13 Interactive Graphics Pipeline
291(26)
13.1 Geometric Transformation of Vertices
293(12)
13.1.1 Model Transformation
293(1)
13.1.2 View Transformation
294(3)
13.1.3 Perspective Projection Transformation
297(2)
13.1.4 Occlusion Resolution
299(5)
13.1.5 Window Coordinate Transformation
304(1)
13.1.6 The Final Transformation
305(1)
13.2 Clipping and Vertex Interpolation of Attributes
305(6)
13.3 Rasterization and Pixel Interpolation of Attributes
311(2)
13.4 Conclusion
313(1)
Bibliography
313(1)
Summary
314(1)
Exercises
315(2)
14 Realism and Performance
317(40)
14.1 Illumination
317(4)
14.2 Shading
321(1)
14.3 Shadows
322(3)
14.4 Texture Mapping
325(7)
14.4.1 Texture to Object Space Mapping
325(4)
14.4.2 Object to Screen Space Mapping
329(1)
14.4.3 Mipmapping
330(2)
14.5 Bump Mapping
332(3)
14.6 Environment Mapping
335(2)
14.7 Transparency
337(2)
14.8 Accumulation Buffer
339(1)
14.9 Back Face Culling
340(2)
14.10 Visibility Culling
342(7)
14.10.1 Bounding Volumes
342(3)
14.10.2 Spatial Subdivision
345(1)
14.10.3 Other Uses
346(3)
14.11 Conclusion
349(1)
Bibliography
350(1)
Summary
351(1)
Exercises
352(5)
15 Graphics Programming
357(10)
15.1 Development of Graphics Processing Unit
357(4)
15.2 Development of Graphics APIs and Libraries
361(2)
15.3 The Modern GPU and CUDA
363(3)
15.3.1 GPU Architecture
363(1)
15.3.2 CUDA Programming Model
364(2)
15.3.3 CUDA Memory Model
366(1)
15.4 Conclusion
366(1)
Bibliography
367(1)
Summary
368(1)
Index 369
Aditi Majumder, Ph.D., is professor at the Department of Computer Science in University of California, Irvine. Her research resides at the junction of computer graphics, computer vision and image processing focusing on computational cameras and displays, virtual and augmented reality, and human computer interaction. She has more than 60 publications in top venues like ACM Siggraph, Eurographics, IEEE Visweek including Best Paper Awards at IEEE Virtual Reality (VR), IEEE Visweek and IEEE Projector Camera Systems (PROCAMS) for her work on multi-projector displays. She also holds around 10 US patents in this domain. She has delivered several invited presentation and keynotes across the world.

Prof. Majumder is silver medalist for academic excellence at Jadavpur University from where she earned her B.E. in Computer Science and Engineering before completing her PhD in Computer Science from University of North Carolina at Chapel Hill in 2003. She has served as Papers Co-Chair for IEEE VR 2011, ACM Virtual Reality Software and Technology (VRST) 2014, IEEE PROCAMS 2009 and 2005, General Chair for ACM VRST 2007 and IEEE VR 2012, Associate Editor in Computer and Graphics and IEEE Computer Graphics and Applications. She also serves as the Equity Advisor for the School of Information and Computer Science at UCI. She has played a key role in developing the first curved screen multi-projector display being marketed by NEC/Alienware currently. She is the recipient of the NSF CAREER award, and was a Link Foundation Fellow in 2001 and Givens Fellow at Argonne National Laboratory from 2002-2003.







M. Gopi

is a professor of Computer Science and Associate Dean at the Bren School of Information and Computer Sciences at University of California, Irvine. His research interests include geometry and topology in computer graphics, massive geometry data management for interactive rendering, and biomedical sensors, data processing, and visualization. His work on representation of mani- folds using single triangle strip, hierarchyless simplification of triangulated man- ifolds, use of redundant representation for big data for interactive rendering, and biomedical image processing have received critical acclaim including best paper awards in two Eurographics conferences and in ICVGIP.Prof. Gopi received his PhD in Computer Science from University of North Carolina at Chapel Hill in 2001. He is a gold medalist for academic excellence at Thiagarajar College of Engineering, a recipient of the Excellence in Teaching Award at UCI and a Link Foundation Fellow. He served as the program co-chair and papers co-chair of ACM Interactive 3D Graphics conference in 2012 and 2013 respectively, area chair for ICVGIP in 2010 and 2012, program co-chair for International Symposium on Visual Computing 2006, an associate editor of the Journal of Graphical Models, a guest editor of IEEE Transactions on Visualization and Computer Graphics and serves in the steering committee of ACM Interactive 3D Graphics.
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