Efficient Computation of Adaptive Threshold Surfaces for Image Binarization
Abstract
The problem of binarization of gray level images acquired under nonuniform illumination is reconsidered. Yanowitz and Bruckstein (1989) proposed to use an adaptive threshold surface, determined by interpolation of the image gray levels at points where the image gradient is high. The rationale is that a high image gradient indicates probable object edges, and there the image values are between the object and background gray levels. The threshold surface was determined by successive overrelaxation as the solution of the Laplace equation. This work proposes a different method to determine an adaptive threshold surface. In this new method, inspired by multiresolution approximation, the threshold surface is constructed with considerably lower computational complexity and is smooth, yielding faster image binarizations and better visual performance.
Cite
Text
Blayvas et al. "Efficient Computation of Adaptive Threshold Surfaces for Image Binarization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990549Markdown
[Blayvas et al. "Efficient Computation of Adaptive Threshold Surfaces for Image Binarization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/blayvas2001cvpr-efficient/) doi:10.1109/CVPR.2001.990549BibTeX
@inproceedings{blayvas2001cvpr-efficient,
title = {{Efficient Computation of Adaptive Threshold Surfaces for Image Binarization}},
author = {Blayvas, Ilya and Bruckstein, Alfred M. and Kimmel, Ron},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2001},
pages = {I:737-742},
doi = {10.1109/CVPR.2001.990549},
url = {https://mlanthology.org/cvpr/2001/blayvas2001cvpr-efficient/}
}