An Effective Method of Threshold Selection for Small Object Image

吴一全; 吴加明; 占必超

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An Effective Method of Threshold Selection for Small Object Image

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摘要

The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.

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《防务技术:英文版》 |2011年第4期|235-242|共8页
作者
吴一全; 吴加明; 占必超;
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作者单位

School of Electronic and Information Engineering;

Nanjing University of Aeronautics and Astronautics;

Science and Technology on Electro-optic Control Laboratory;

Institute of Electro-optic Equipment of AVIC;

State Key Laboratory of Novel Software Technology;

Nanjing University;

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原文格式 PDF
正文语种 chi
中图分类模式识别与装置;
关键词
information processing; small infrared target detection; image segmentation; threshold selection; 2-D histogram oblique segmentation; fast recursive algorithm;

An Effective Method of Threshold Selection for Small Object Image

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