Abstract
Various cost aggregation methods have been developed for finding correspondences between stereo pairs, but their high complexity is still a problem for practical use. In this paper, we propose a confidence-based hierarchical structure to reduce the complexity of the cost aggregation algorithms. Aggregating matching costs for each pixel with the smallest support window, we estimate confidence levels. The confidence values are used to decide which pixel needs additional cost aggregations. For the pixels of small confidence, we iteratively supplement their matching costs by using larger support windows. Our experiments show that our approach reduces computational time and improves the quality of output disparity images.
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Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011–0030822).
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Jung, JI., Ho, YS. (2013). Confidence-Based Hierarchical Support Window for Fast Local Stereo Matching. In: The Era of Interactive Media. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3501-3_29
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DOI: https://doi.org/10.1007/978-1-4614-3501-3_29
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