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| Content Provider | Springer Nature Link |
|---|---|
| Author | Zhao, Wen Yi Chellappa, Rama |
| Copyright Year | 2001 |
| Abstract | In this paper, we present a symmetric shape-from-shading (SFS) approach to recover both shape and albedo for symmetric objects. Lambertian surfaces with unknown varying albedo and orthographic projections are assumed. In our formulation of symmetric SFS, we have two image irradiance equations. One is the standard equation used in SFS, and the other is a self-ratio image irradiance equation. This new image irradiance equation relates the self-ratio image which is defined as the ratio of two-halves of the input image to light source and surface shape. The introduction of the self-ratio image facilitates the direct use of symmetry cue. Based on the self-ratio image, a new model-based symmetric source-from-shading algorithm is also presented. We then propose symmetric SFS algorithms to recover both shape and albedo from a single image and present experimental results.The new symmetric SFS scheme has one important property: the existence of a unique (global) solution which consists of unique (local) solutions at each point simultaneously obtained using the intensity information at that point and the surrounding local region and the assumption of a C $^{2}$ surface. Proofs for the existence of a unique solution in the cases of unknown constant and non-constant albedos are provided. |
| Starting Page | 55 |
| Ending Page | 75 |
| Page Count | 21 |
| File Format | |
| ISSN | 09205691 |
| Journal | International Journal of Computer Vision |
| Volume Number | 45 |
| Issue Number | 1 |
| e-ISSN | 15731405 |
| Language | English |
| Publisher | Kluwer Academic Publishers |
| Publisher Date | 2001-01-01 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Artificial Intelligence (incl. Robotics) Computer Imaging, Graphics and Computer Vision Image Processing Automation and Robotics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Vision and Pattern Recognition Software |
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