Visual reconstruction 1st Edition by Andrew Blake, Andrew Zisserman ISBN 0262524066 9780262524063

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ISBN 10: 0262524066
ISBN 13: 9780262524063
Author: Andrew Blake, Andrew Zisserman

Visual Reconstruction presents a unified and highly original approach to the treatment of continuity in vision. It introduces, analyzes, and illustrates two new concepts. The first–the weak continuity constraint–is a concise, computational formalization of piecewise continuity. It is a mechanism for expressing the expectation that visual quantities such as intensity, surface color, and surface depth vary continuously almost everywhere, but with occasional abrupt changes. The second concept–the graduated nonconvexity algorithm–arises naturally from the first. It is an efficient, deterministic (nonrandom) algorithm for fitting piecewise continuous functions to visual data. The book first illustrates the breadth of application of reconstruction processes in vision with results that the authors’ theory and program yield for a variety of problems. The mathematics of weak continuity and the graduated nonconvexity (GNC) algorithm are then developed carefully and progressively.

Visual reconstruction 1st Edition Table of contents:

Part I: Fundamentals of Visual Reconstruction

2. Image Formation and Camera Geometry

   • The Basics of Image Formation
   • Camera Models and Geometry
   • Coordinate Systems and Transformations
   • Calibration and the Role of Camera Parameters

3. Feature Detection and Matching

   • Detecting and Extracting Key Features from Images
   • Descriptors and Matching Techniques
   • Matching Challenges in Real-World Images
   • The Importance of Feature Consistency

4. Stereo Vision and Depth Estimation

   • Principles of Stereo Vision
   • Depth Perception and Disparity Maps
   • Algorithms for Stereo Matching
   • Applications of Stereo Vision in Reconstruction

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Part II: Techniques for 3D Reconstruction

5. From 2D to 3D: Structure from Motion (SfM)

   • The Basics of Structure from Motion
   • Tracking and Motion Estimation
   • Reconstructing 3D Structures from 2D Images
   • SfM Algorithms and Optimization Techniques

6. Multi-View Geometry

   • The Geometry of Multiple Views
   • Epipolar Geometry and Correspondences
   • Reconstruction from Multiple Images
   • Techniques for Multi-View Stereo and Dense Reconstruction

7. Model-Based Reconstruction

   • Using Models for 3D Reconstruction
   • Geometric and Photometric Models in Reconstruction
   • Fitting Models to Data
   • Case Studies in Model-Based Visual Reconstruction

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Part III: Advanced Topics in Visual Reconstruction

8. Motion and Deformation Analysis

   • Understanding Motion in Images
   • Reconstruction of Dynamic Scenes
   • Analyzing Deformations in 3D Models
   • Applications in Motion Capture and Animation

9. 3D Scene Reconstruction from Image Sequences

   • Approaches for Scene Reconstruction
   • Integrating Temporal Information in Reconstruction
   • Techniques for Handling Real-Time Data and Dynamic Environments
   • Applications in Robotics and Autonomous Systems

10. Photometric and Radiometric Reconstruction

    • The Role of Photometric Information in Reconstruction
    • Radiometric Calibration and Correction
    • Texture Mapping and Illumination Models
    • Real-World Applications of Photometric Reconstruction

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Part IV: Applications of Visual Reconstruction

11. Visual Reconstruction in Robotics

    • The Role of Visual Reconstruction in Robotic Vision
    • 3D Reconstruction for Path Planning and Navigation
    • Applications in Robotic Grasping and Manipulation

12. Computer Graphics and Virtual Reality

    • Visual Reconstruction for Computer Graphics
    • Building Virtual Environments from Real-World Data
    • Applications in Virtual Reality and Augmented Reality

13. Medical Imaging and Archaeology

    • Applications in Medical Image Reconstruction
    • Visual Reconstruction in Archaeology and Cultural Heritage
    • Imaging and Analysis of Complex Structures

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Conclusion
14. The Future of Visual Reconstruction
– Emerging Trends in Visual Reconstruction
– Advances in Computational Vision and 3D Imaging
– The Role of Deep Learning in Visual Reconstruction
– Final Thoughts and Directions for Future Research

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