Two-dimensional entropic segmentation
Non-Linear Analysis
Image segmentation by histogram thresholding using hierarchical cluster analysis
Pattern Recognition Letters
On minimum variance thresholding
Pattern Recognition Letters
Thresholding based on variance and intensity contrast
Pattern Recognition
Image Segmentation Using Excess Entropy
Journal of Signal Processing Systems
Polarimetric SAR image segmentation with B-splines and a new statistical model
Multidimensional Systems and Signal Processing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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Variance-based thresholding method is a very effective technology for image segmentation. However, its performance is limited in traditional one-dimensional and two-dimensional scheme. In this paper, a novel two-dimensional variance thresholding scheme to improve image segmentation performance is proposed. The two-dimensional histogram of the original and local average image is projected to one-dimensional space in the proposed scheme firstly, and then the variance-based criterion is constructed for threshold selection. The experimental results on bi-level and multilevel thresholding for synthetic and real-world images demonstrate the success of the proposed image thresholding scheme, as compared with the Otsu method, the two-dimensional Otsu method and the minimum class variance thresholding method.