Robust uncalibrated stereo rectification with constrained geometric distortions (USR-CGD)
- Authors
- Ko, Hyunsuk; Shim, Han Suk; Choi, Ouk; Kuo, C. -C. Jay
- Issue Date
- Apr-2017
- Publisher
- Elsevier BV
- Keywords
- Projective rectification; Regularization; Homography; Epipolar geometry; Fundamental matrix; Geometric distortion; Constrained optimization
- Citation
- Image and Vision Computing, v.60, pp.98 - 114
- Indexed
- SCIE
SCOPUS
- Journal Title
- Image and Vision Computing
- Volume
- 60
- Start Page
- 98
- End Page
- 114
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/10073
- DOI
- 10.1016/j.imavis.2017.01.001
- ISSN
- 0262-8856
- Abstract
- A novel algorithm for uncalibrated stereo image-pair rectification under the constraint of geometric distortion, called USR-CGD, is presented in this work. Although it is straightforward to define a rectifying transformation (or homography) given the epipolar geometry, many existing algorithms have unwanted geometric distortions as a side effect. To obtain rectified images with reduced geometric distortions while maintaining a small rectification error, we parameterize the homography by considering the influence of various kinds of geometric distortions. Next, we define several geometric measures and incorporate them into a new cost function as regularization terms for parameter optimization. Finally, we propose a constrained adaptive optimization scheme to allow a balanced performance between the rectification error and the geometric error. Extensive experimental results are provided to demonstrate the superb performance of the proposed USR-CGD method, which outperforms existing algorithms by a significant margin. (C) 2017 Elsevier B.V. All rights reserved.
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