9 August 2018 A credible depth estimation method based on superpixel constraint matching
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Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 1080605 (2018) https://doi.org/10.1117/12.2502906
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China
Abstract
In order to reduce the errors in depth estimation, a credible depth estimation method based on superpixel constraint matching is proposed. It consists of normalized binocular image disparity optimization, credible granularity region segmentation and similarity measure of granularity region. This method segments the normalized binocular images finely by using the superpixel granulation method, and divides the binocular image into a large number of excellent granularity regions. To get the best match for each granularity partitioned, the correlative matching area is obtained by polar line constraint matching. And then the matching similarity measure function is used to achieve the best superpixel granularity regional matching results in binocular images, so as to find the two-dimensional correspondence of each granularity region. Finally, realize the depth information estimation of binocular parallax images. The experimental results show that this method can obviously reduce the errors of depth estimation in traditional methods.
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Chao Zhang, Chao Zhang, Yunxiu Zhao, Yunxiu Zhao, Cheng Han, Cheng Han, Ye Bai, Ye Bai, } "A credible depth estimation method based on superpixel constraint matching", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080605 (9 August 2018); doi: 10.1117/12.2502906; https://doi.org/10.1117/12.2502906
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