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Structured Light Methods: From Land to Undersea Open Access


Other title
Structured Light
Underwater Calibration
Type of item
Degree grantor
University of Alberta
Author or creator
Supervisor and department
Herb Yang (Computing Science)
Examining committee member and department
Martin Jagersand (Computing Science)
Aksel Hallin (Physics)
Hong Zhang (Computing Science)
Department of Computing Science

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Extracting 3D geometry of an object from 2D images has been a popular topic in computer vision for decades. Many methods have been proposed to solve this problem with high accuracy and structured light methods are one of the most commonly used. Despite their high accuracy, there are limitations for existing methods. First, most existing methods can either be applied to obtain dense correspondences but limited to static scenes, or be applied to dynamic scenes but limited to sparse correspondences. Second, existing methods cannot be applied to scenes with global illuminations such as inter-reflection and projector defocus. Last but not least, existing structured light methods are rarely applied to underwater applications due to the difficulties of underwater camera calibration. Motivated by the above limitations, a new structured light method is presented to establish dense correspondences for dynamic scenes. Many simulated and real datasets are used to test the robustness of this method. Moreover, another novel structured light method is presented in this thesis to account for global illuminations such as inter-reflection, subsurface scattering and severe projector defocus. The experimental results demonstrate that the proposed method consistently outperforms two of the state-of-the-art methods. To address the limitation in underwater applications, two new underwater camera calibration methods are presented by using the physically correct refraction model. All the experimental results of applying these two methods are evaluated against the ground truth, and the simulated experiments are compared to the methods that produces the best results. The comparison demonstrates that our results are promising. The most significant advantage of these two methods is that no calibration object is required, which can be very difficult to access when the cameras are deployed undersea. Finally, a multi-camera multi-projector system is designed and developed to monitor the undersea habitat. Since 2014, the system has been deployed along the coast of B.C., collecting data that can be shared with researchers from around the world.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
X. Chen and Y.H.Yang, Recovering Stereo Depth Maps using a Single Gaussian Blurred Structured Light Pattern," Canadian Conference on Computer and Robot Vision, May 28-31, 2013, pp. 295-302.X. Chen and Y.H. Yang, Scene Adaptive Structured Light using Error Detection and Correction," Pattern Recognition, Vol. 48, Issue 1, 2015, pp. 220-230.X. Chen and Y.H. Yang, Two-view Camera Housing Parameters Calibration for Multi-Layer Flat Refractive Interface," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, June 24-27, 2014, Columbus, Ohio.

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