| Description |
1 online resource (47 p.) |
| Series |
Spotlight Series ; v.62 |
|
Spotlight series.
|
| Contents |
Intro -- Copyright -- Series Page -- Preface -- 1 Introduction -- 1.1 Matrices in imaging -- 1.2 Singular value decomposition -- 1.3 Applications to imaging problems -- 2 Camera Calibration -- 2.1 Camera model -- 2.2 Direct linear transform method -- 3 Multiple View Geometry -- 3.1 Image to image projections -- 3.2 Fundamental matrix -- 3.3 Triangulation -- 4 Spectral Clustering -- 5 Simulation of Partially Coherent Systems -- 5.1 Optical system simulation -- 5.2 Partial coherence -- 5.3 Model-based optical proximity correction -- 6 Computing the SVD -- 6.1 Introduction |
|
6.2 Bidiagonalization -- 6.3 QR algorithm -- 7 Appendix: Code Listings -- 7.1 Camera calibration -- 7.2 Spectral clustering -- 7.3 Partial coherence -- References -- Author Biography |
| Summary |
Singular value decomposition (SVD) is one of the most useful results of linear algebra with many applications. However, it is rarely discussed in books and classes. This Spotlight describes SVD, its applications to imaging, and its computation in a single introductory text. Sample code is included for illustration |
| Notes |
Description based upon print version of record |
| Subject |
Singular value decomposition.
|
|
Imaging systems.
|
|
Imaging systems
|
|
Singular value decomposition
|
| Form |
Electronic book
|
| ISBN |
9781510647015 |
|
1510647015 |
|
