Introduction to visual computing

Introduction to visual computing

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Bibliographic Details
Main Author: Majumder, Aditi
Corporate Author: ProQuest (Firm)
Format: Electronic eBook
Language:English
Published: [Place of publication not identified] : Apple Academic Press Inc, 2016.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)
Table of Contents:
  • Machine generated contents note: 1. Data
  • 1.1. Visualization
  • 1.2. Discretization
  • 1.2.1. Sampling
  • 1.2.2. Quantization
  • 1.3. Representation
  • 1.3.1. Geometric Data
  • 1.4. Noise
  • 1.5. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 2. Techniques
  • 2.1. Interpolation
  • 2.1.1. Linear Interpolation
  • 2.1.2. Bilinear Interpolation
  • 2.2. Geometric Intersections
  • 2.3. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 3. Convolution
  • 3.1. Linear Systems
  • 3.1.1. Response of a Linear System
  • 3.1.2. Properties of Convolution
  • 3.2. Linear Filters
  • 3.2.1. All, Low, Band and High Pass Filters
  • 3.2.2. Designing New Filters
  • 3.2.3. 2D Filter Separability
  • 3.2.4. Correlation and Pattern Matching
  • 3.3. Implementation Details
  • 3.4. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 4. Spectral Analysis
  • 4.1. Discrete Fourier Transform
  • 4.1.1. Why Sine and Cosine Waves?
  • 4.2. Polar Notation
  • 4.2.1. Properties
  • 4.2.2. Example Analysis of Signals
  • 4.3. Periodicity of Frequency Domain
  • 4.4. Aliasing
  • 4.5. Extension for 2D Interpretation
  • 4.5.1. Effect of Periodicity
  • 4.5.2. Notch Filter
  • 4.5.3. Example of Aliasing
  • 4.6. Duality
  • 4.7. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 5. Feature Detection
  • 5.1. Edge Detection
  • 5.1.1. Edgel Detectors
  • 5.1.2. Multi-Resolution Edge Detection
  • 5.1.3. Aggregating Edgels
  • 5.2. Feature Detection
  • 5.3. Other Non-Linear Filters
  • 5.4. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 6. Geometric Transformations
  • 6.1. Homogeneous Coordinates
  • 6.2. Linear Transformations
  • 6.3. Euclidean and Affine Transformations
  • 6.3.1. Translation
  • 6.3.2. Rotation
  • 6.3.3. Scaling
  • 6.3.4. Shear
  • 6.3.5. Some Observations
  • 6.4. Concatenation of Transformations
  • 6.4.1. Scaling About the Center
  • 6.4.2. Rotation About an Arbitrary Axis
  • 6.5. Coordinate Systems
  • 6.5.1. Change of Coordinate Systems
  • 6.6. Properties of Concatenation
  • 6.6.1. Global vs Local Coordinate System
  • 6.7. Projective Transformation
  • 6.8. Degrees of Freedom
  • 6.9. Non-Linear Transformations
  • 6.10. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 7. Pinhole Camera
  • 7.1. Model
  • 7.1.1. Camera Calibration
  • 7.1.2. 3D Depth Estimation
  • 7.1.3. Homography
  • 7.2. Considerations in the Practical Camera
  • 7.3. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 8. Epipolar Geometry
  • 8.1. Background
  • 8.2. Correspondences in Multi-View Geometry
  • 8.3. Fundamental Matrix
  • 8.3.1. Properties
  • 8.3.2. Estimating Fundamental Matrix
  • 8.3.3. Camera Setup Akin to Two Frontal Eyes
  • 8.4. Essential Matrix
  • 8.5. Rectification
  • 8.6. Applying Epipolar Geometry
  • 8.6.1. Depth from Disparity
  • 8.6.2. Depth from Optical Flow
  • 8.7. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 9. Light
  • 9.1. Radiometry
  • 9.1.1. Bidirectional Reflectance Distribution Function
  • 9.1.2. Light Transport Equation
  • 9.2. Photometry and Color
  • 9.2.1. CIE XYZ Color Space
  • 9.2.2. Perceptual Organization of CIE XYZ Space
  • 9.2.3. Perceptually Uniform Color Spaces
  • 9.3. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 10. Color Reproduction
  • 10.1. Modeling Additive Color Mixtures
  • 10.1.1. Color Gamut of a Device
  • 10.1.2. Tone Mapping Operator
  • 10.1.3. Intensity Resolution
  • 10.1.4. Example Displays
  • 10.2. Color Management
  • 10.2.1. Gamut Transformation
  • 10.2.2. Gamut Matching
  • 10.3. Modeling Subtractive Color Mixture
  • 10.4. Limitations
  • 10.4.1. High Dynamic Range Imaging
  • 10.4.2. Multi-Spectral Imaging
  • 10.5. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 11. Photometric Processing
  • 11.1. Histogram Processing
  • 11.1.1. Handling Color Images
  • 11.2. Image Composition
  • 11.2.1. Image Blending
  • 11.2.2. Image Cuts
  • 11.3. Photometric Stereo
  • 11.3.1. Handling Shadows
  • 11.3.2. Computing Illumination Directions
  • 11.3.3. Handling Color
  • 11.4. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 12. Diverse Domain
  • 12.1. Modeling
  • 12.2. Processing
  • 12.3. Rendering
  • 12.4. Application
  • 12.5. Conclusion
  • Bibliography
  • 13. Interactive Graphics Pipeline
  • 13.1. Geometric Transformation of Vertices
  • 13.1.1. Model Transformation
  • 13.1.2. View Transformation
  • 13.1.3. Perspective Projection Transformation
  • 13.1.4. Occlusion Resolution
  • 13.1.5. Window Coordinate Transformation
  • 13.1.6. Final Transformation
  • 13.2. Clipping and Vertex Interpolation of Attributes
  • 13.3. Rasterization and Pixel Interpolation of Attributes
  • 13.4. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 14. Realism and Performance
  • 14.1. Illumination
  • 14.2. Shading
  • 14.3. Shadows
  • 14.4. Texture Mapping
  • 14.4.1. Texture to Object Space Mapping
  • 14.4.2. Object to Screen Space Mapping
  • 14.4.3. Mipmapping
  • 14.5. Bump Mapping
  • 14.6. Environment Mapping
  • 14.7. Transparency
  • 14.8. Accumulation Buffer
  • 14.9. Back Face Culling
  • 14.10. Visibility Culling
  • 14.10.1. Bounding Volumes
  • 14.10.2. Spatial Subdivision
  • 14.10.3. Other Uses
  • 14.11. Conclusion
  • Bibliography
  • Summary
  • Exercises
  • 15. Graphics Programming
  • 15.1. Development of Graphics Processing Unit
  • 15.2. Development of Graphics APIs and Libraries
  • 15.3. Modern GPU and CUDA
  • 15.3.1. GPU Architecture
  • 15.3.2. CUDA Programming Model
  • 15.3.3. CUDA Memory Model
  • 15.4. Conclusion
  • Bibliography
  • Summary.

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