Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
VARFIT: a fortran-77 program for fitting variogram models by weighted least squares
Computers & Geosciences
Semivariogram fitting with linear programming
Computers & Geosciences
Multi-scale binarization of images
Pattern Recognition Letters
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A novel image thresholding method based on Parzen window estimate
Pattern Recognition
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Robust threshold estimation for images with unimodal histograms
Pattern Recognition Letters
Geo-thresholding for segmentation of fluorescent microscopic cell images
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
Unsupervised range-constrained thresholding
Pattern Recognition Letters
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In this paper, an algorithm for image thresholding based on semivariance analysis is presented. The rationale of the approach is to binarize an image such that it best reproduces the original image variation across several spatial scales. The method can be alternatively viewed as one identifying the binary image that best approximate the overall level of edgeness measured across multiple scales in the original image. A comparison with seven other thresholding methods is presented for 2 synthetic images and 22 Non-Destructive Testing (NDT) grey level images. The results indicate that the proposed method is highly competitive. Performance of the proposed method in relation to the image content is also discussed.