Robust stereo matching under radiometric variations based on cumulative distributions of gradients

Il Lyong Jung, Jae Young Sim, Chang-Su Kim, Sang Uk Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

We propose a robust stereo matching algorithm for images captured under varying radiometric conditions, such as exposure and lighting variations, based on the cumulative distributions of gradients. The gradient operator extracts local changes in pixel values, which are less sensitive to radiometric variations than the original pixel values. Moreover, the cumulative distribution function (CDF) of gradient vectors reflects the ranks of edge strength levels, and corresponding pixels in stereo images tend to have similar ranks regardless of radio-metric conditions. Therefore, we design the matching cost function based on the dissimilarity of gradient CDF values. However, since multiple pixels in an image may have the same gradient CDF value, we further constrain the correspondence matching by checking the dissimilarity of gradient orientations. Finally, to estimate an accurate disparity at each pixel, we adaptively aggregate matching costs using the color similarity and the geometric proximity of neighboring pixels. Experimental results demonstrate that the proposed algorithm provides more accurate disparities than conventional algorithms, especially under varying lighting conditions.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages2082-2085
Number of pages4
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 2013 Sep 152013 Sep 18

Other

Other2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period13/9/1513/9/18

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Keywords

  • 3-D image processing
  • cumulative distribution function
  • gradient-based rank matching
  • radio-metric variations
  • Stereo matching

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Jung, I. L., Sim, J. Y., Kim, C-S., & Lee, S. U. (2013). Robust stereo matching under radiometric variations based on cumulative distributions of gradients. In 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings (pp. 2082-2085). [6738429] https://doi.org/10.1109/ICIP.2013.6738429