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Introduction to visual computing : core concepts in computer vision, graphics, and image processing / Aditi Majumder, M. Gopi.

By: Contributor(s): Material type: TextTextPublisher: Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, [2017]Description: 1 online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781482244915 (hardback : alk. paper)
Subject(s): DDC classification:
  • 006.6 23
LOC classification:
  • QA76.65 .M34 2017
Online resources:
Contents:
I Fundamentals; 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; II Image Based Visual Computing; 3. Convolution; 3.1 Linear Systems; 3.1.1 Response of a Linear System; 3.1.2 Properties of Convolution. 3.2 Linear Filters3.2.1 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. 6.4.2 Rotation About an Arbitrary Axis6.5 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. The Pinhole Camera; 7.1 The 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 matrix8.3.1 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; IV Radiometric Visual Computing; 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.
Summary: "Introduction to Visual Computing: Core Concepts in Computer Vision, Graphics, and Image Processing covers the fundamental concepts of visual computing. Whereas past books have treated these concepts within the context of specific fields such as computer graphics, computer vision or image processing, this book offers a unified view of these core concepts, thereby providing a unified treatment of computational and mathematical methods for creating, capturing, analyzing and manipulating visual data (e.g. 2D images, 3D models). Fundamentals covered in the book include convolution, Fourier transform, filters, geometric transformations, epipolar geometry, 3D reconstruction, color and the image synthesis pipeline. The book is organized in four parts. The first part provides an exposure to different kinds of visual data (e.g. 2D images, videos and 3D geometry) and the core mathematical techniques that are required for their processing (e.g. interpolation and linear regression.) The second part of the book on Image Based Visual Computing deals with several fundamental techniques to process 2D images (e.g. convolution, spectral analysis and feature detection) and corresponds to the low level retinal image processing that happens in the eye in the human visual system pathway. The next part of the book on Geometric Visual Computing deals with the fundamental techniques used to combine the geometric information from multiple eyes creating a 3D interpretation of the object and world around us (e.g. transformations, projective and epipolar geometry, and 3D reconstruction). This corresponds to the higher level processing that happens in the brain combining information from both the eyes thereby helping us to navigate through the 3D world around us. The last two parts of the book cover Radiometric Visual Computing and Visual Content Synthesis. These parts focus on the fundamental techniques for processing information arising from the interaction of light with objects around us, as well as the fundamentals of creating virtual computer generated worlds that mimic all the processing presented in the prior sections. The book is written for a 16 week long semester course and can be used for both undergraduate and graduate teaching, as well as a reference for professionals."--Provided by publisher.
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Includes bibliographical references.

I Fundamentals; 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; II Image Based Visual Computing; 3. Convolution; 3.1 Linear Systems; 3.1.1 Response of a Linear System; 3.1.2 Properties of Convolution. 3.2 Linear Filters3.2.1 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. 6.4.2 Rotation About an Arbitrary Axis6.5 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. The Pinhole Camera; 7.1 The 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 matrix8.3.1 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; IV Radiometric Visual Computing; 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.

"Introduction to Visual Computing: Core Concepts in Computer Vision, Graphics, and Image Processing covers the fundamental concepts of visual computing. Whereas past books have treated these concepts within the context of specific fields such as computer graphics, computer vision or image processing, this book offers a unified view of these core concepts, thereby providing a unified treatment of computational and mathematical methods for creating, capturing, analyzing and manipulating visual data (e.g. 2D images, 3D models). Fundamentals covered in the book include convolution, Fourier transform, filters, geometric transformations, epipolar geometry, 3D reconstruction, color and the image synthesis pipeline. The book is organized in four parts. The first part provides an exposure to different kinds of visual data (e.g. 2D images, videos and 3D geometry) and the core mathematical techniques that are required for their processing (e.g. interpolation and linear regression.) The second part of the book on Image Based Visual Computing deals with several fundamental techniques to process 2D images (e.g. convolution, spectral analysis and feature detection) and corresponds to the low level retinal image processing that happens in the eye in the human visual system pathway. The next part of the book on Geometric Visual Computing deals with the fundamental techniques used to combine the geometric information from multiple eyes creating a 3D interpretation of the object and world around us (e.g. transformations, projective and epipolar geometry, and 3D reconstruction). This corresponds to the higher level processing that happens in the brain combining information from both the eyes thereby helping us to navigate through the 3D world around us. The last two parts of the book cover Radiometric Visual Computing and Visual Content Synthesis. These parts focus on the fundamental techniques for processing information arising from the interaction of light with objects around us, as well as the fundamentals of creating virtual computer generated worlds that mimic all the processing presented in the prior sections. The book is written for a 16 week long semester course and can be used for both undergraduate and graduate teaching, as well as a reference for professionals."--Provided by publisher.

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