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1.

図書

図書
J.R. Parker
出版情報: Indianapolis, Ind. : Wiley, c2011  xxiv, 480 p. ; 24 cm
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2.

図書

図書
David A. Forsyth ... [et al.] (eds.)
出版情報: Berlin : Springer, c1999  viii, 345 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1681
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3.

図書

図書
edited by C.H. Chen, L.F. Pau, P.S.P. Wang
出版情報: Singapore : World Scientific, c1999  xxiii, 1019 p. ; 26 cm
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4.

図書

図書
by Alireza Moini
出版情報: Boston : Kluwer Academic, c2000  xv, 300 p. ; 24 cm
シリーズ名: The Kluwer international series in engineering and computer science ; SECS 526
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5.

図書

図書
Roland Chin, Ting-Chuen Pong (eds.)
出版情報: Berlin ; New York : Springer, c1997  2 v. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1351-1352
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The ACCV'98 volumes present together a total of 58
revised full papers and 112
revised posters selected from over 300 submissions
The papers are organized in topical sections on biometry
physics-based vision
color vision
robot vision and navigation
OCR and applications
low-level processing
active vision
face and hand posture recognition
segmentation and grouping
computer vision and virtual reality
motion analysis and object recognition and modeling
The ACCV'98 volumes present together a total of 58
revised full papers and 112
revised posters selected from over 300 submissions
6.

図書

図書
Roberto Cipolla, Peter Giblin
出版情報: Cambridge ; New York : Cambridge University Press, 2000  viii, 184 p. ; 26 cm
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7.

図書

図書
edited by H.R. Wu and K.R. Rao
出版情報: Boca Raton, FL : Taylor & Francis, 2006  xxxv, 600 p. ; 24 cm
シリーズ名: Signal processing and communications series ; 28
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List of Contributors
Acknowledgments
Preface
Picture Coding and Human Visual System Fundamentals / I:
Digital Picture Compression and Coding Structure / 1:
Introduction to Digital Picture Coding / 1.1:
Characteristics of Picture Data / 1.2:
Compression and Coding Techniques / 1.3:
Picture Quantization / 1.4:
Rate-Distortion Theory / 1.5:
Human Visual Systems / 1.6:
Digital Picture Coding Standards and Systems / 1.7:
Summary / 1.8:
Fundamentals of Human Vision and Vision Modeling / 2:
Introduction / 2.1:
A Brief Overview of the Visual System / 2.2:
Color Vision / 2.3:
Luminance and the Perception of Light Intensity / 2.4:
Spatial Vision and Contrast Sensitivity / 2.5:
Temporal Vision and Motion / 2.6:
Visual Modeling / 2.7:
Conclusions / 2.8:
Coding Artifacts and Visual Distortions / 3:
Blocking Effect / 3.1:
Basis Image Effect / 3.3:
Blurring / 3.4:
Color Bleeding / 3.5:
Staircase Effect / 3.6:
Ringing / 3.7:
Mosaic Patterns / 3.8:
False Contouring / 3.9:
False Edges / 3.10:
MC Mismatch / 3.11:
Mosquito Effect / 3.12:
Stationary Area Fluctuations / 3.13:
Chrominance Mismatch / 3.14:
Video Scaling and Field Rate Conversion / 3.15:
Deinterlacing / 3.16:
Picture Quality Assessment and Metrics / 3.17:
Video Quality Testing / 4:
Subjective Assessment Methodologies / 4.1:
Selection of Test Materials / 4.3:
Selection of Participants - Subjects / 4.4:
Experimental Design / 4.5:
International Test Methods / 4.6:
Objective Assessment Methods / 4.7:
Perceptual Video Quality Metrics - A Review / 4.8:
Quality Factors / 5.1:
Metric Classification / 5.3:
Pixel-Based Metrics / 5.4:
The Psychophysical Approach / 5.5:
The Engineering Approach / 5.6:
Metric Comparisons / 5.7:
Conclusions and Perspectives / 5.8:
Philosophy of Picture Quality Scale / 6:
Objective Picture Quality Scale for Image Coding / 6.1:
Application of PQS to a Variety of Electronic Images / 6.2:
Various Categories of Image Systems / 6.3:
Study at ITU / 6.4:
Conclusion / 6.5:
Structural Similarity Based Image Quality Assessment / 7:
Structural Similarity and Image Quality / 7.1:
The Structural SIMilarity (SSIM) Index / 7.2:
Image Quality Assessment Based on the SSIM Index / 7.3:
Discussions / 7.4:
Vision Model Based Digital Video Impairment Metrics / 8:
Vision Modeling for Impairment Measurement / 8.1:
Perceptual Blocking Distortion Metric / 8.3:
Perceptual Ringing Distortion Measure / 8.4:
Computational Models for Just-Noticeable Difference / 8.5:
JND with DCT Subbands / 9.1:
JND with Pixels / 9.3:
JND Model Evaluation / 9.4:
No-Reference Quality Metric for Degraded and Enhanced Video / 9.5:
State-of-the-Art for No-Reference Metrics / 10.1:
Quality Metric Components and Design / 10.3:
No-Reference Overall Quality Metric / 10.4:
Performance of the Quality Metric / 10.5:
Conclusions and Future Research / 10.6:
Video Quality Experts Group / 11:
Formation / 11.1:
Goals / 11.2:
Phase I / 11.3:
Phase II / 11.4:
Continuing Work and Directions / 11.5:
Perceptual Coding and Processing of Digital Pictures / 11.6:
HVS Based Perceptual Video Encoders / 12:
Noise Visibility and Visual Masking / 12.1:
Architectures for Perceptual Based Coding / 12.3:
Standards-Specific Features / 12.4:
Salience/Maskability Pre-Processing / 12.5:
Application to Multi-Channel Encoding / 12.6:
Perceptual Image Coding / 13:
A Perceptual Distortion Metric Based Image Coder / 13.1:
Model Calibration / 13.3:
Performance Evaluation / 13.4:
Perceptual Lossless Coder / 13.5:
Foveated Image and Video Coding / 13.6:
Foveated Human Vision and Foveated Image Processing / 14.1:
Foveation Methods / 14.2:
Scalable Foveated Image and Video Coding / 14.3:
Artifact Reduction by Post-Processing in Image Compression / 14.4:
Image Compression and Coding Artifacts / 15.1:
Reduction of Blocking Artifacts / 15.3:
Reduction of Ringing Artifacts / 15.4:
Reduction of Color Bleeding in DCT Block-Coded Video / 15.5:
Analysis of the Color Bleeding Phenomenon / 16.1:
Description of the Post-Processor / 16.3:
Experimental Results - Concluding Remarks / 16.4:
Error Resilience for Video Coding Service / 17:
Introduction to Error Resilient Coding Techniques / 17.1:
Error Resilient Coding Methods Compatible with MPEG-2 / 17.2:
Methods for Concealment of Cell Loss / 17.3:
Experimental Procedure / 17.4:
Experimental Results / 17.5:
Critical Issues and Challenges / 17.6:
Picture Coding Structures / 18.1:
Vision Modeling Issues / 18.2:
Spatio-Temporal Masking in Video Coding / 18.3:
Picture Quality Assessment / 18.4:
Challenges in Perceptual Coder Design / 18.5:
Codec System Design Optimization / 18.6:
VQM Performance Metrics / 18.7:
Metrics Relating to Model Prediction Accuracy / A.1:
Metrics Relating to Prediction Monotonicity of a Model / A.2:
Metrics Relating to Prediction Consistency / A.3:
MATLAB Source Code / A.4:
Supplementary Analyses / A.5:
Index
List of Contributors
Acknowledgments
Preface
8.

図書

図書
Daniel Scharstein
出版情報: Berlin ; New York : Springer, c1999  xv, 163 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 1583
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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:
9.

図書

図書
editor, C.H. Chen
出版情報: [London] : Imperial College Press , Hackensack, N.J. ; Singapore : World Scientific, c2010  xx, 776 p. ; 26 cm
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10.

図書

図書
Sven Behnke
出版情報: Berlin ; Tokyo : Springer, c2003  xii, 224 p. ; 24 cm
シリーズ名: Lecture notes in computer science ; 2766
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目次情報: 続きを見る
Foreword
Preface
Introduction / 1:
Motivation / 1.1:
Importance of Visual Perception / 1.1.1:
Performance of the Human Visual System / 1.1.2:
Limitations of Current Computer Vision Systems / 1.1.3:
Iterative Interpretation - Local Interactions in a Hierarchy / 1.1.4:
Organization of the Thesis / 1.2:
Contributions / 1.3:
Theory / Part I:
Neurobiological Background / 2:
Visual Pathways / 2.1:
Feature Maps / 2.2:
Layers / 2.3:
Neurons / 2.4:
Synapses / 2.5:
Discussion / 2.6:
Conclusions / 2.7:
Related Work / 3:
Hierarchical Image Models / 3.1:
Generic Signal Decompositions / 3.1.1:
Neural Networks / 3.1.2:
Generative Statistical Models / 3.1.3:
Recurrent Models / 3.2:
Models with Lateral Interactions / 3.2.1:
Models with Vertical Feedback / 3.2.2:
Models with Lateral and Vertical Feedback / 3.2.3:
Neural Abstraction Pyramid Architecture / 3.3:
Overview / 4.1:
Hierarchical Network Structure / 4.1.1:
Distributed Representations / 4.1.2:
Local Recurrent Connectivity / 4.1.3:
Iterative Refinement / 4.1.4:
Formal Description / 4.2:
Simple Processing Elements / 4.2.1:
Shared Weights / 4.2.2:
Discrete-Time Computation / 4.2.3:
Various Transfer Functions / 4.2.4:
Example Networks / 4.3:
Local Contrast Normalization / 4.3.1:
Binarization of Handwriting / 4.3.2:
Activity-Driven Update / 4.3.3:
Invariant Feature Extraction / 4.3.4:
Unsupervised Learning / 4.4:
Learning a Hierarchy of Sparse Features / 5.1:
Network Architecture / 5.2.1:
Initialization / 5.2.2:
Hebbian Weight Update / 5.2.3:
Competition / 5.2.4:
Learning Hierarchical Digit Features / 5.3:
Digit Classification / 5.4:
Supervised Learning / 5.5:
Nearest Neighbor Classifier / 6.1:
Decision Trees / 6.1.2:
Bayesian Classifier / 6.1.3:
SupportVectorMachines / 6.1.4:
Bias/Variance Dilemma / 6.1.5:
Feed-Forward Neural Networks / 6.2:
Error Backpropagation / 6.2.1:
Improvements to Backpropagation / 6.2.2:
Regularization / 6.2.3:
Recurrent Neural Networks / 6.3:
Backpropagation through Time / 6.3.1:
Real-Time Recurrent Learning / 6.3.2:
Difficulty of Learning Long-Term Dependencies / 6.3.3:
Random Recurrent Networks with Fading Memories / 6.3.4:
Robust Gradient Descent / 6.3.5:
Applications / 6.4:
Recognition of Meter Values / 7:
Introduction to Meter Value Recognition / 7.1:
Swedish Post Database / 7.2:
Preprocessing / 7.3:
Filtering / 7.3.1:
Normalization / 7.3.2:
Block Classification / 7.4:
NetworkArchitectureandTraining / 7.4.1:
Experimental Results / 7.4.2:
Digit Recognition / 7.5:
Digit Preprocessing / 7.5.1:
Combination with Block Recognition / 7.5.2:
Binarization of Matrix Codes / 7.6:
Introduction to Two-Dimensional Codes / 8.1:
Canada Post Database / 8.2:
Adaptive Threshold Binarization / 8.3:
Image Degradation / 8.4:
Learning Binarization / 8.5:
Learning Iterative Image Reconstruction / 8.6:
Introduction to Image Reconstruction / 9.1:
Super-resolution / 9.2:
NIST Digits Dataset / 9.2.1:
Architecture for Super-resolution / 9.2.2:
Filling-in Occlusions / 9.2.3:
MNIST Dataset / 9.3.1:
Architecture for Filling-in of Occlusions / 9.3.2:
Noise Removal and Contrast Enhancement / 9.3.3:
Reconstruction from a Sequence of Degraded Digits / 9.4.1:
Face Localization / 9.5.1:
Introduction to Face Localization / 10.1:
Face Database and Preprocessing / 10.2:
conclusions / 10.3:
Summary and Conclusions / 11:
Short Summary of Contributions / 11.1:
Future Work / 11.2:
implementation Options / 11.3.1:
Using More Complex Processing Elements / 11.3.2:
Integration into Complete Systems / 11.3.3:
References
Index
Foreword
Preface
Introduction / 1:
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