The Principle of Two-Dimensional Maximum Between-Class Variance (
Otsu) and Its Improvement
For comparison purposed the results obtained using classical
Otsu method are used.
At the center of the proposed model, an image enhancement algorithm called Min-Max Gray Level Discrimination (M2GLD) is put forward as a preprocessing step to improve the
Otsu binarization approach, followed by shape analyses for meliorating the crack detection performance.
(2016) implemented a SC approach to segment the soft brain tissues using a two-step procedure based on
Otsu's thresholding [8].
In this paper, in order to improve the effect of segmentation and reduce computational cost of two-dimensional
Otsu algorithm, a novel fast image segmentation method using two-dimensional
Otsu based on estimation of distributed algorithm is proposed.
The variance in
Otsu method will be used as a reference in this paper.
This process begins with thresholding the blue canal of the RGB (Red, Green, and Blue) image using the
Otsu thresholding method to obtain a binary image.