Computer Vision and Applications: A Guide for Students and Practitioners,Concise Edition
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Table of contents
- Contentsv
- Prefacexi
- Contributorsxv
- Chapter 1. Introduction1
- 1.1 Components of a vision system1
- 1.2 Imaging systems2
- 1.3 Signal processing for computer vision3
- 1.4 Pattern recognition for computer vision4
- 1.5 Performance evaluation of algorithms5
- 1.6 Classes of tasks6
- 1.7 References8
- Part I: Sensors and Imaging9
- Chapter 2. Radiation and Illumination11
- 2.1 Introduction12
- 2.2 Fundamentals of electromagnetic radiation13
- 2.3 Radiometric quantities17
- 2.4 Fundamental concepts of photometry27
- 2.5 Interaction of radiation with matter31
- 2.6 Illumination techniques46
- 2.7 References51
- Chapter 3. Imaging Optics53
- 3.1 Introduction54
- 3.2 Basic concepts of geometric optics54
- 3.3 Lenses56
- 3.4 Optical properties of glasses66
- 3.5 Aberrations67
- 3.6 Optical image formation75
- 3.7 Wave and Fourier optics80
- 3.8 References84
- Chapter 4. Radiometry of Imaging85
- 4.1 Introduction85
- 4.2 Observing surfaces86
- 4.3 Propagating radiance88
- 4.4 Radiance of imaging91
- 4.5 Detecting radiance94
- 4.6 Concluding summary108
- 4.7 References109
- Chapter 5. Solid-State Image Sensing111
- 5.1 Introduction112
- 5.2 Fundamentals of solid-state photosensing113
- 5.3 Photocurrent processing120
- 5.4 Transportation of photosignals127
- 5.5 Electronic signal detection130
- 5.6 Architectures of image sensors134
- 5.7 Color vision and color imaging139
- 5.8 Practical limitations of semiconductor photosensors146
- 5.9 Conclusions148
- 5.10 References149
- Chapter 6. Geometric Calibration of Digital Imaging Systems153
- 6.1 Introduction153
- 6.2 Calibration terminology154
- 6.3 Parameters influencing geometrical performance155
- 6.4 Optical systems model of image formation157
- 6.5 Camera models158
- 6.6 Calibration and orientation techniques163
- 6.7 Photogrammetric applications170
- 6.8 Summary173
- 6.9 References173
- Chpater 7. Three-Dimensional Imaging Techniques177
- 7.1 Introduction178
- 7.2 Characteristics of 3-D sensors179
- 7.3 Triangulation182
- 7.4 Time-of-flight (TOF) of modulated light196
- 7.5 Optical Interferometry (OF)199
- 7.6 Conclusion205
- 7.7 References205
- Part II. Signal Processing and Pattern Recognition209
- Chapter 8. Representation of Multidimensional Signals211
- 8.1 Introduction212
- 8.2 Continuous signals212
- 8.3 Discrete signals215
- 8.4 Relation between continuous and discrete signals224
- 8.5 Vector spaces and unitary transforms232
- 8.6 Continuous Fourier transform (FT)237
- 8.7 The discrete Fourier transform (DFT)246
- 8.8 Scale of signals252
- 8.9 Scale space and diffusion260
- 8.10 Multigrid representations267
- 8.11 References271
- Chapter 9. Neighborhood Operators273
- 9.1 Basics274
- 9.2 Linear shift-invariant filters278
- 9.3 Recursive filters285
- 9.4 Classes of nonlinear filters292
- 9.5 Local averaging296
- 9.6 Interpolation311
- 9.7 Edge detection325
- 9.8 Tensor representation of simple neighborhoods335
- 9.9 References344
- Chapter 10. Motion347
- 10.1 Introduction347
- 10.2 Basics: flow and correspondence349
- 10.3 Optical flow-based motion estimation358
- 10.4 Quadrature filter techniques372
- 10.5 Correlation and matching379
- 10.6 Modeling of flow fields382
- 10.7 References392
- Chapter 11. Three-Dimensional Imaging Algorithms397
- 11.1 Introduction397
- 11.2 Stereopsis398
- 11.3 Depth-from-focus414
- 11.4 References435
- Chapter 12. Design of Nonlinear Diffusion Filters439
- 12.1 Introduction439
- 12.2 Filter design440
- 12.3 Parameter selection448
- 12.4 Extensions451
- 12.5 Relations to variational image restoration452
- 12.6 Summary454
- 12.7 References454
- Chapter 13. Variational Adaptive Smoothing and Segmentation459
- 13.1 Introduction459
- 13.2 Processing of two- and three-dimensional images463
- 13.3 Processing of vector-valued images474
- 13.4 Processing of image sequences476
- 13.5 References480
- Chapter 14. Morphological Operators483
- 14.1 Introduction483
- 14.2 Preliminaries484
- 14.3 Basic morphological operators489
- 14.4 Advanced morphological operators495
- 14.5 References515
- Chapter 15. Probabilistic Modeling in Computer Vision517
- 15.1 Introduction517
- 15.2 Why probabilistic models?518
- 15.3 Object recognition as probabilistic modeling519
- 15.4 Model densities524
- 15.5 Practical issues536
- 15.6 Summary, conclusions, and discussion538
- 15.7 References539
- Chapter 16. Fuzzy Image Processing541
- 16.1 Introduction541
- 16.2 Fuzzy image understanding548
- 16.3 Fuzzy image processing systems553
- 16.4 Theoretical components of fuzzy image processing556
- 16.5 Selected application examples564
- 16.6 Conclusions570
- 16.7 References571
- Chapter 17. Neural Net Computing for Image Processing577
- 17.1 Introduction577
- 17.2 Multilayer perceptron (MLP)579
- 17.3 Self-organizing neural networks585
- 17.4 Radial-basis neural networks (RBNN)590
- 17.5 Transformation radial-basis networks (TRBNN)593
- 17.6 Hopfield neural networks596
- 17.7 Application examples of neural networks601
- 17.8 Concluding remarks604
- 17.9 References605
- Part III: Application Gallery607
- A Application Gallery609
- A1 Object Recognition with Intelligent Cameras610
- A2 3-D Image Metrology of Wing Roots612
- A3 Quality Control in a Shipyard614
- A4 Topographical Maps of Microstructures616
- A5 Fast 3-D Full Body Scanning for Humans and Other Objects618
- A6 Reverse Engineering Using Optical Range Sensors620
- A7 3-D Surface Reconstruction from Image Sequences622
- A8 Motion Tracking624
- A9 Tracking FuzzyŽ Storms in Doppler Radar Images626
- A10 3-D Model-Driven Person Detection628
- A11 Knowledge-Based Image Retrieval630
- A12 Monitoring Living Biomass with in situ Microscopy632
- A13 Analyzing Size Spectra of Oceanic Air Bubbles634
- A14 Thermography to Measure Water Relations of Plant Leaves636
- A15 Small-Scale Air-Sea Interaction with Thermography638
- A16 Optical Leaf Growth Analysis640
- A17 Analysis of Motility Assay Data642
- A18 Fluorescence Imaging of Air-Water Gas Exchange644
- A19 Particle-Tracking Velocimetry646
- A20 Analyzing Particle Movements at Soil Interfaces648
- A21 3-D Velocity Fields from Flow Tomography Data650
- A22 Cloud Classification Analyzing Image Sequences652
- A23 NOX Emissions Retrieved from Satellite Images654
- A24 Multicolor Classification of Astronomical Objects656
- A25 Model-Based Fluorescence Imaging658
- A26 Analyzing the 3-D Genome Topology660
- A27 References662
- Index667
Based on the highly successful 3-volume reference Handbook of Computer Vision and Applications, this concise edition covers in a single volume the entire spectrum of computer vision ranging form the imaging process to high-end algorithms and applications. This book consists of three parts, including an application gallery, and is accompanied by a companion website
* Bridges the gap between theory and practical applications
* Covers modern concepts in computer vision as well as modern developments in imaging sensor technology
* Presents a unique interdisciplinary approach covering different areas of modern science
* An accompanying companion website provides 3-D models, image material in Tiff format and Movies in Apple Quicktime, Microsoft AVI, or MPEG format
* Bridges the gap between theory and practical applications
* Covers modern concepts in computer vision as well as modern developments in imaging sensor technology
* Presents a unique interdisciplinary approach covering different areas of modern science
* An accompanying companion website provides 3-D models, image material in Tiff format and Movies in Apple Quicktime, Microsoft AVI, or MPEG format