Lecture Notes in Computer Science : Lecture Notes in Computer Science - Daniel Scharstein

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Lecture Notes in Computer Science

By: Daniel Scharstein

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Paperback | 9 June 1999

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188 Pages

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23.5 x 15.88 x 0.64

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Image-based rendering, as an area of overlap between computer graphics and computer vision, uses computer vision techniques to aid in sythesizing new views of scenes. Image-based rendering methods are having a substantial impact on the field of computer graphics, and also play an important role in the related field of multimedia systems, for applications such as teleconferencing, remote instruction and surgery, virtual reality and entertainment.
The book develops a novel way of formalizing the view synthesis problem under the full perspective model, yielding a clean, linear warping equation. It shows new techniques for dealing with visibility issues such as partial occlusion and "holes". Furthermore, the author thoroughly re-evaluates the requirements that view synthesis places on stereo algorithms and introduces two novel stereo algorithms specifically tailored to the application of view synthesis.

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Non-Fiction Computing & I.T.Computer Science Image Processing Arts & Entertainment Photography & Photographs Photographic Equipment & Techniques Artificial Intelligence Computer Vision Medicine Medicine in General Graphical & Digital Media Applications

Databases Data Capture & Analysis Computer Networking & Communications Business Applications

Introduction	p. 1
The Problem	p. 2
Applications	p. 2
The Computer Graphics Approach	p. 5
Avoiding the Model	p. 6
A Review of Stereo Vision	p. 8
Camera Model and Image Formation	p. 8
Stereo Geometry	p. 10
The Correspondence Problem	p. 13
The Epipolar Constraint	p. 14
A Simple Stereo Geometry	p. 16
Rectification	p. 17
Example: SSD	p. 19
Contributions and Outline	p. 22
A Survey of Image-Based Rendering and Stereo	p. 23
Image-Based Rendering	p. 23
View Synthesis Based on Stereo	p. 24
View Interpolation	p. 27
Mosaics and Layered Representations	p. 29
Stereo	p. 32
A Framework for Stereo	p. 32
Preprocessing	p. 33
Matching Cost	p. 33
Evidence Aggregation	p. 34
Disparity Selection	p. 35
Sub-Pixel Disparity Computation	p. 35
Diffusion-Based Techniques	p. 36
Other Techniques	p. 37
Promising Recent Approaches	p. 37
Computer Vision Books	p. 38
View Synthesis	p. 41
Geometry	p. 42
Three-View Rectification	p. 42
The Linear Warping Equation	p. 44
Computing the Rectifying Homographies	p. 45
Synthesizing a New View	p. 47
Resolving Visibility	p. 47
Holes and Sampling Gaps	p. 47
Combining Information from Both Images	p. 48
Adjusting Intensities	p. 49
Filling Holes	p. 50
The View Synthesis Algorithm	p. 51
Limitations of the Approach	p. 52
Experiments	p. 53
Image-Based Scene Representations	p. 60
Summary	p. 61
Re-evaluating Stereo	p. 63
Traditional Applications of Stereo	p. 63
Automated Cartography	p. 64
Robot Navigation	p. 64
3D Reconstruction	p. 65
3D Recognition	p. 66
Visual Servoing	p. 66
Full vs. Weak Calibration	p. 67
Comparison of Requirements	p. 68
Stereo for View Synthesis	p. 68
Accuracy	p. 69
Correct vs. Realistic Views	p. 71
Areas of Uniform Intensities	p. 72
Geometric Constraints	p. 73
Interpolated Views	p. 75
Extrapolated Views	p. 77
General Views and the Aperture Problem	p. 79
Assigning Canonical Depth Interpretations	p. 80
Does Adding More Cameras Help?	p. 80
Partial Occlusion	p. 81
Summary	p. 85
Gradient-Based Stereo	p. 87
Similarity and Confidence	p. 88
Displacement-Oriented Stereo	p. 89
The Evidence Measure	p. 90
Comparing Two Gradient Vectors	p. 91
Comparing Gradient Fields	p. 93
Computing Gradients of Discrete Images	p. 94
Accumulating the Measure	p. 96
Experiments	p. 97
Observing E_¿ for Interesting Displacements	p. 98
Stereo: 1D Search Range	p. 98
General Motion: 2D Search Range	p. 105
Computing Disparity Maps for View Synthesis	p. 105
Occlusion Boundaries	p. 107
Detecting Partially Occluded Points and Uniform Regions	p. 109
Extrapolating the Disparities	p. 109
Efficiency	p. 109
Discussion and Possible Extensions	p. 110
Summary	p. 111
Stereo Using Diffusion	p. 113
Disparity Space	p. 114
The SSD Algorithm and Boundary Blurring	p. 116
Aggregating Support by Diffusion	p. 121
The Membrane Model	p. 122
Support Function for the Membrane Model	p. 123
Diffusion with Local Stopping	p. 125
A Bayesian Model of Stereo Matching	p. 127
The Prior Model	p. 127
The Measurement Model	p. 129
Explicit Local Distribution Model	p. 131
Experiments	p. 134
Discussion and Possible Extensions	p. 141
Summary	p. 144
Conclusion	p. 145
Contributions in View Synthesis	p. 145
Contributions in Stereo	p. 146
Extensions and Future Work	p. 146
Bibliography	p. 149
Table of Contents provided by Publisher. All Rights Reserved.

Lecture Notes in Computer Science

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Paperback

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