Per-Pixel Translational Symmetry Detection, Optimization, and Segmentation
Abstract
We present a novel method for translational symmetry detection, optimization, and symmetry object segmentation in façade images. Unlike most previous methods, our detection algorithm accumulates pixel-level correspondence in translation space. Thus it does not rely on feature point detection and handles patterns with low repetition counts. To improve the robustness with multiple interfering symmetries, we introduce an image-space global optimization, which resolves multiple per-pixel symmetry lattices. We then propose a learning-based method that generates refined segmentation of foreground symmetry objects of arbitrary shapes, with the aid of the per-pixel symmetry information. Our proposed method is accurate, robust and efficient as demonstrated by an extensive evaluation using a large façade image database.
Cite
Text
Zhao et al. "Per-Pixel Translational Symmetry Detection, Optimization, and Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012. doi:10.1109/CVPR.2012.6247717Markdown
[Zhao et al. "Per-Pixel Translational Symmetry Detection, Optimization, and Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012.](https://mlanthology.org/cvpr/2012/zhao2012cvpr-per/) doi:10.1109/CVPR.2012.6247717BibTeX
@inproceedings{zhao2012cvpr-per,
title = {{Per-Pixel Translational Symmetry Detection, Optimization, and Segmentation}},
author = {Zhao, Peng and Yang, Lei and Zhang, Honghui and Quan, Long},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2012},
pages = {526-533},
doi = {10.1109/CVPR.2012.6247717},
url = {https://mlanthology.org/cvpr/2012/zhao2012cvpr-per/}
}