Image Segmentation Using Two-dimensional Extension of Minimum Within-class Variance Criterion
Fangyan Nie, Jianqi Li, Tianyi Tu, Pingfeng Zhang · International Journal of Signal Processing Image Processing and Pattern Recognition · 2013
Thresholding based on variance analysis of gray levels histogram is a very effective technology for image segmentation.However, its performance is limited in conventional forms.In this paper, a novel method based on two-dimensional extension of within-class variance is proposed to improve segmentation performance.The two-dimensional histogram of the original and local average image is projected to one-dimensional space firstly, and then the minimum within-class variance criterion is constructed for threshold selection.The effectiveness of the proposed method is demonstrated by using examples from the synthetic and real-word images.