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Robust surface normal estimation via greedy sparse regression

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2014-01-07
Authors/Contributors
Abstract
Photometric Stereo (PST) is a widely used technique of estimating surface normals from an image set. However, it often produces inaccurate results for non-Lambertian surface reflectance. In this study, PST is reformulated as a sparse recovery problem where non-Lambertian errors are explicitly identified and corrected. We show that such a problem can be accurately solved via a greedy algorithm called Orthogonal Matching Pursuit (OMP). Furthermore, we introduce a smoothness constraint by expanding the pixel-wise sparse PST into a joint sparse recovery problem where several adjacent pixels are processed simultaneously, and employ a Sequential Compressive - Multiple Signal Classification (SeqCS-MUSIC) algorithm based on Simultaneous Orthogonal Matching Pursuit (S-OMP) to reach a robust solution. The performance of OMP and SeqCS-MUSIC is evaluated on synthesized and real-world datasets, and we found that these greedy algorithms are overall more robust to non-Lambertian errors than other state-of-the-art sparse approaches with little loss of efficiency.
Document
Identifier
etd8213
Copyright statement
Copyright is held by the author.
Permissions
The author granted permission for the file to be printed, but not for the text to be copied and pasted.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Drew, Mark S.
Member of collection
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etd8213_MZhang.pdf 9.09 MB

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