View Synthesis Using Stereo Vision

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View Synthesis Using Stereo Vision / by Daniel Scharstein

版:

1st ed. 1999

資料種別:

電子ブック

形態:

XVI, 172 p.

シリーズ名:

Lecture Notes in Computer Science ; 1583

著者名:

ISBN:

9783540487258 [3540487255]
9783662196274 [3662196271]
9783540661597 [354066159X]

書誌ID:

EB00003576

子書誌情報

フルテキスト

所蔵情報

書誌詳細

注記:

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.

主題:

Artificial Intelligence; Computer Graphics; Computer Vision; Computer and Information Systems Applications; Multimedia Information Systems

目次情報:

Introduction / 1：

The Problem / 1.1：

Applications / 1.1.1：

The Computer Graphics Approach / 1.1.2：

Avoiding the Model / 1.1.3：

A Review of Stereo Vision / 1.2：

Camera Model and Image Formation / 1.2.1：

Stereo Geometry / 1.2.2：

The Correspondence Problem / 1.2.3：

The Epipolar Constraint / 1.2.4：

A Simple Stereo Geometry / 1.2.5：

Rectification / 1.2.6：

Example: SSD / 1.2.7：

Contributions and Outline / 1.3：

A Survey of Image-Based Rendering and Stereo / 2：

Image-Based Rendering / 2.1：

View Synthesis Based on Stereo / 2.1.1：

View Interpolation / 2.1.2：

Mosaics and Layered Representations / 2.1.3：

Stereo / 2.2：

A Framework for Stereo / 2.2.1：

Preprocessing / 2.2.2：

Matching Cost / 2.2.3：

Evidence Aggregation / 2.2.4：

Disparity Selection / 2.2.5：

Sub-Pixel Disparity Computation / 2.2.6：

Diffusion-Based Techniques / 2.2.7：

Other Techniques / 2.2.8：

Promising Recent Approaches / 2.2.9：

Computer Vision Books / 2.3：

View Synthesis / 3：

Geometry / 3.1：

Three-View Rectification / 3.1.1：

The Linear Warping Equation / 3.1.2：

Computing the Rectifying Homographies / 3.1.3：

Synthesizing a New View / 3.2：

Resolving Visibility / 3.2.1：

Holes and Sampling Gaps / 3.2.2：

Combining Information from Both Images / 3.2.3：

Adjusting Intensities / 3.2.4：

Filling Holes / 3.2.5：

The View Synthesis Algorithm / 3.2.6：

Limitations of the Approach / 3.2.7：

Experiments / 3.3：

Image-Based Scene Representations / 3.4：

Summary / 3.5：

Re-evaluating Stereo / 4：

Traditional Applications of Stereo / 4.1：

Automated Cartography / 4.1.1：

Robot Navigation / 4.1.2：

3D Reconstruction / 4.1.3：

3D Recognition / 4.1.4：

Visual Servoing / 4.1.5：

Full vs. Weak Calibration / 4.1.6：

Comparison of Requirements / 4.1.7：

Stereo for View Synthesis / 4.2：

Accuracy / 4.3：

Correct vs. Realistic Views / 4.4：

Areas of Uniform Intensities / 4.5：

Geometric Constraints / 4.5.1：

Interpolated Views / 4.5.2：

Extrapolated Views / 4.5.3：

General Views and the Aperture Problem / 4.5.4：

Assigning Canonical Depth Interpretations / 4.5.5：

Does Adding More Cameras Help? / 4.5.6：

Partial Occlusion / 4.6：

Gradient-Based Stereo / 4.7：

Similarity and Confidence / 5.1：

Displacement-Oriented Stereo / 5.2：

The Evidence Measure / 5.3：

Comparing Two Gradient Vectors / 5.3.1：

Comparing Gradient Fields / 5.3.2：

Computing Gradients of Discrete Images / 5.3.3：

Accumulating the Measure / 5.4：

Stereo: 1D Search Range / 5.5：

General Motion: 2D Search Range / 5.5.3：

Computing Disparity Maps for View Synthesis / 5.6：

Occlusion Boundaries / 5.6.1：

Detecting Partially Occluded Points and Uniform Regions / 5.6.2：

Extrapolating the Disparities / 5.6.3：

Efficiency / 5.7：

Discussion and Possible Extensions / 5.8：

Stereo Using Diffusion / 5.9：

Disparity Space / 6.1：

The SSD Algorithm and Boundary Blurring / 6.2：

Aggregating Support by Diffusion / 6.3：

The Membrane Model / 6.3.1：

Support Function for the Membrane Model / 6.3.2：

Diffusion with Local Stopping / 6.4：

A Bayesian Model of Stereo Matching / 6.5：

The Prior Model / 6.5.1：

The Measurement Model / 6.5.2：

Explicit Local Distribution Model / 6.5.3：

Conclusion / 6.6：

Contributions in View Synthesis / 7.1：

Contributions in Stereo / 7.2：

Extensions and Future Work / 7.3：

Bibliography

Introduction / 1：

The Problem / 1.1：

Applications / 1.1.1：

The Computer Graphics Approach / 1.1.2：

Avoiding the Model / 1.1.3：

A Review of Stereo Vision / 1.2：

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