A Generalization of Otsu’s Method and Minimum Error Thresholding
Abstract
We present Generalized Histogram Thresholding (GHT), a simple, fast, and effective technique for histogram-based image thresholding. GHT works by performing approximate maximum a posteriori estimation of a mixture of Gaussians with appropriate priors. We demonstrate that GHT subsumes three classic thresholding techniques as special cases: Otsu's method, Minimum Error Thresholding (MET), and weighted percentile thresholding. GHT thereby enables the continuous interpolation between those three algorithms, which allows thresholding accuracy to be improved significantly. GHT also provides a clarifying interpretation of the common practice of coarsening a histogram's bin width during thresholding. We show that GHT outperforms or matches the performance of all algorithms on a recent challenge for handwritten document image binarization (including deep neural networks trained to produce per-pixel binarizations), and can be implemented in a dozen lines of code or as a trivial modification to Otsu's method or MET.
Cite
Text
Barron. "A Generalization of Otsu’s Method and Minimum Error Thresholding." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58558-7_27Markdown
[Barron. "A Generalization of Otsu’s Method and Minimum Error Thresholding." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/barron2020eccv-generalization/) doi:10.1007/978-3-030-58558-7_27BibTeX
@inproceedings{barron2020eccv-generalization,
title = {{A Generalization of Otsu’s Method and Minimum Error Thresholding}},
author = {Barron, Jonathan T.},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58558-7_27},
url = {https://mlanthology.org/eccv/2020/barron2020eccv-generalization/}
}