[1]LONG Peng,LU Huaxiang.Global threshold segmentation technique guided by prior knowledge with asymmetric variance[J].CAAI Transactions on Intelligent Systems,2015,10(5):663-668.[doi:10.11992/tis.201412022]
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Global threshold segmentation technique guided by prior knowledge with asymmetric variance
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
10
Number of periods:
2015 5
Page number:
663-668
Column:
学术论文—人工智能基础
Public date:
2015-10-25
- Title:
- Global threshold segmentation technique guided by prior knowledge with asymmetric variance
- CLC:
- TP751
- DOI:
- 10.11992/tis.201412022
- Abstract:
- Image segmentation is a fundamental step in image processing, and threshold segmentation is the simplest and most widely used method among the segmentation methods. The classic Otsu method is deemed as one of the best methods for general real world images with regard to uniformity and shape measure. However, a lot of research shows that, for two classes of image with large variance difference, the threshold seriously deviates from the opti-mum threshold and inclines to the type with larger variance. In this paper, optimal Otsu criteria and the properties of an existing improved version are analyzed, then a novel criterion of optimization is proposed by combining prior knowledge about the variance discrepancy between background and foreground. The method is compared with the current non-between-class variance threshold methods and some improved Otsu threshold methods. The results show that our method is optimal, with no need for variable parameters.
- References:
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Last Update:
2015-11-16