ML Anthology

Robust Change-Detection by Normalised Gradient-Correlation

Robert O'Callaghan, Tetsuji Haga
CVPR 2007
doi:10.1109/CVPR.2007.383516 /cvpr/2007/oaposcallaghan2007cvpr-robust/

Abstract

A novel algorithm for robustly segmenting changes between different images of a scene is presented. This computationally efficient algorithm is based on a non-linear comparison of gradient structure in overlapping image-regions and offers intrinsic invariance to changing illumination, without recourse to background-model adaptation. High accuracy is demonstrated on test video data with and without illumination changes. The technique is applicable to motion-segmentation as well as measuring longer-term object-changes.

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Cite

Text

O'Callaghan and Haga. "Robust Change-Detection by Normalised Gradient-Correlation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383516

Markdown

[O'Callaghan and Haga. "Robust Change-Detection by Normalised Gradient-Correlation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/oaposcallaghan2007cvpr-robust/) doi:10.1109/CVPR.2007.383516

BibTeX

@inproceedings{oaposcallaghan2007cvpr-robust,
  title     = {{Robust Change-Detection by Normalised Gradient-Correlation}},
  author    = {O'Callaghan, Robert and Haga, Tetsuji},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383516},
  url       = {https://mlanthology.org/cvpr/2007/oaposcallaghan2007cvpr-robust/}
}