Two-dimensional extension of variance-based thresholding for image segmentation

被引：32
|
作者：
Nie, Fangyan [1 ]
Wang, Yonglin [1 ]
Pan, Meisen [1 ]
Peng, Guanghan [2 ]
Zhang, Pingfeng [1 ]
机构：
[1] Hunan Univ Arts & Sci, Coll Comp Sci & Technol, Inst Graph & Image Proc Technol, Changde 415000, Peoples R China
[2] Hunan Univ Arts & Sci, Coll Phys & Elect Sci, Changde 415000, Peoples R China
来源：
MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING | 2013年 / 24卷 / 03期
关键词：
Image segmentation; Variance-based thresholding; Two-dimensional gray level histogram; Otsu method; Minimum class variance thresholding; ENTROPY;
D O I：
10.1007/s11045-012-0174-7
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：485 / 501
页数：17
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