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Content Provider | IEEE Xplore Digital Library |
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Author | Yilin Wang Ke Wang Dunn, E. Frahm, J.-M. |
Copyright Year | 2014 |
Description | Author affiliation: Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Yilin Wang; Ke Wang; Dunn, E.; Frahm, J.-M.) |
Abstract | We develop a sequential optimal sampling framework for stereo disparity estimation by adapting the Sequential Probability Ratio Test (SPRT) model. We operate over local image neighborhoods by iteratively estimating single pixel disparity values until sufficient evidence has been gathered to either validate or contradict the current hypothesis regarding local scene structure. The output of our sampling is a set of sampled pixel positions along with a robust and compact estimate of the set of disparities contained within a given region. We further propose an efficient plane propagation mechanism that leverages the pre-computed sampling positions and the local structure model described by the reduced local disparity set. Our sampling framework is a general pre-processing mechanism aimed at reducing computational complexity of disparity search algorithms by ascertaining a reduced set of disparity hypotheses for each pixel. Experiments demonstrate the effectiveness of the proposed approach when compared to state of the art methods. |
Starting Page | 485 |
Ending Page | 492 |
File Size | 872184 |
Page Count | 8 |
File Format | |
ISBN | 9781479951185 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.2014.69 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2014-06-23 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Estimation Adaptation models Robustness Complexity theory Computational modeling Approximation methods Image segmentation |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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