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Proceedings Paper

A credible depth estimation method based on superpixel constraint matching
Author(s): Chao Zhang; Yunxiu Zhao; Cheng Han; Ye Bai
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Paper 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.

Paper Details

Date Published: 9 August 2018
PDF: 8 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080605 (9 August 2018); doi: 10.1117/12.2502906
Show Author Affiliations
Chao Zhang, Changchun Univ. of Science and Technology (China)
Yunxiu Zhao, Changchun Univ. of Science and Technology (China)
Cheng Han, Changchun Univ. of Science and Technology (China)
Ye Bai, Changchun Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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