A statistical approach to sparse multi-scale phase-based stereo

Date

2007-09-01

Author

Ulusoy, İlkay

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In this study, a multi-scale phase based sparse disparity algorithm and a probabilistic model for matching uncertain phase are proposed. The features used are oriented edges extracted using steerable filters. Feature correspondences are estimated using phase-similarity at multiple scale using a magnitude weighting scheme. In order to achieve sub-pixel accuracy in disparity, we use a fine tuning procedure which employs the phase difference between corresponding feature points. We also derive a probabilistic model, where phase uncertainty is trained using data from a single image pair. The model is used to provide stable matches. The disparity algorithm and the probabilistic phase uncertainty model are verified on various stereo image pairs. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Subject Keywords

Signal Processing, Software, Artificial Intelligence, Computer Vision and Pattern Recognition

URI

https://hdl.handle.net/11511/42958

Journal

PATTERN RECOGNITION

DOI

https://doi.org/10.1016/j.patcog.2006.10.019

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

İ. Ulusoy, “A statistical approach to sparse multi-scale phase-based stereo,” PATTERN RECOGNITION, pp. 2504–2520, 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42958.