Abstract
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.
Keywords
Fundamental matrix (linear differential equation)PhotogrammetryProjective geometryComputer scienceEpipolar geometryProjection (relational algebra)Orthographic projectionComputationRepresentation (politics)Tensor (intrinsic definition)Algebra over a fieldKey (lock)Computer visionArtificial intelligenceGeometryAlgebraic geometryMathematicsAlgorithmImage (mathematics)Pure mathematics
Affiliated Institutions
Related Publications
Principles of Algebraic Geometry
A comprehensive, self-contained treatment presenting general results of the theory. Establishes a geometric intuition and a working facility with specific geometric practices. E...
Effective representation using ICA for face recognition robust to local distortion and partial occlusion
The performance of face recognition methods using subspace projection is directly related to the characteristics of their basis images, especially in the cases of local distorti...
Tensor Decompositions and Applications
This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or N-way array. Decompositions...
Matrix Analysis
Linear algebra and matrix theory are fundamental tools in mathematical and physical science, as well as fertile fields for research. This new edition of the acclaimed text prese...
Topics in Matrix Analysis
Building on the foundations of its predecessor volume, Matrix Analysis, this book treats in detail several topics in matrix theory not included in the previous volume, but with ...
Publication Info
- Year
- 2004
- Type
- book
- Citations
- 20403
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
20403
OpenAlex
Cite This
Richard Hartley,
Andrew Zisserman
(2004).
Multiple View Geometry in Computer Vision.
Cambridge University Press eBooks
.
https://doi.org/10.1017/cbo9780511811685
Identifiers
- DOI
- 10.1017/cbo9780511811685