A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
On minimum error thresholding and its implementations
Pattern Recognition Letters
An analysis of histogram-based thresholding algorithms
CVGIP: Graphical Models and Image Processing
Image thresholding: some new techniques
Signal Processing
Automatic threshold selection based on histogram modes and a discriminant criterion
Machine Vision and Applications
Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy
Pattern Recognition Letters
Engineering Applications of Artificial Intelligence
Ridler and Calvard's, Kittler and Illingworth's and Otsu's methods for image thresholding
Pattern Recognition Letters
Novel KNN-motivation-PSO and its application to image segmentation
Proceedings of the CUBE International Information Technology Conference
Medical image thresholding using online trained neural networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Expert Systems with Applications: An International Journal
Maximum similarity thresholding
Digital Signal Processing
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In order to select an optimal threshold for image thresholding that is relatively robust to the presence of skew and heavy-tailed class-conditional distributions, we propose two median-based approaches: one is an extension of Otsu's method and the other is an extension of Kittler and Illingworth's minimum error thresholding. We provide theoretical interpretation of the new approaches, based on mixtures of Laplace distributions. The two extensions preserve the methodological simplicity and computational efficiency of their original methods, and in general can achieve more robust performance when the data for either class is skew and heavy-tailed. We also discuss some limitations of the new approaches.