ML Anthology

Stereovision Bias Removal by Autocorrelation

Yang Cheng, Larry H. Matthies
WACV 2015 pp. 1153-1160
doi:10.1109/WACV.2015.158 /wacv/2015/cheng2015wacv-stereovision/

Abstract

Sub pixel interpolation of stereo disparity is essential to achieve adequate range resolution for many applications, especially in autonomous navigation. Sub pixel interpolation is plagued by systematic biases caused by pixel-locking, foreshortening, and scaling phenomena. Prior work on this problem has produced partial solutions or solutions that are undesirably slow for real-time applications. We describe a new algorithm ? Stereovision Bias Removal by Autocorrelation (SBRA) ? to correct these biases. SBRA addresses all three of these causes of bias, achieving 0.02 pixel RMS disparity error in synthetic stereo image data and a significant error reduction on real stereo images for which no ground truth is available. SBRA is simple and fast, increasing the runtime of a sum square difference (SSD) stereo matching algorithm by about 10%.

WACV Semantic Scholar

Cite

Text

Cheng and Matthies. "Stereovision Bias Removal by Autocorrelation." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015. doi:10.1109/WACV.2015.158

Markdown

[Cheng and Matthies. "Stereovision Bias Removal by Autocorrelation." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015.](https://mlanthology.org/wacv/2015/cheng2015wacv-stereovision/) doi:10.1109/WACV.2015.158

BibTeX

@inproceedings{cheng2015wacv-stereovision,
  title     = {{Stereovision Bias Removal by Autocorrelation}},
  author    = {Cheng, Yang and Matthies, Larry H.},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2015},
  pages     = {1153-1160},
  doi       = {10.1109/WACV.2015.158},
  url       = {https://mlanthology.org/wacv/2015/cheng2015wacv-stereovision/}
}