非监督层次化模糊相关的人体红外图像分割

尹诗白; 孔垂涵; 王一斌

doi:10.3788/OPE.20182606.1542

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非监督层次化模糊相关的人体红外图像分割

信息科学 | 更新时间：2020-07-05

- 非监督层次化模糊相关的人体红外图像分割
- Unsupervised hierarchical human target infrared image segmentation through fuzzy correlation
- 光学精密工程 2018年26卷第6期页码：1542-1550
- 作者机构：
  
  1.西南财经大学经济信息工程学院, 四川成都 611130
  2.四川师范大学工学院, 四川成都 610101
- 作者简介：
  
  [ "尹诗白(1984-), 女, 湖北潜江人, 博士, 副教授, 2006年和2009年分别于西安工业大学获得学士和硕士学位, 2013年于长安大学获得博士学位, 主要从事图像处理, 机器视觉方面的研究。E-mail:shibai.yin@gmail.com" ]
  [ "王一斌(1982-), 男, 河南人, 讲师, 分别于2006年、2009年、2016年在西北工业大学获得学士、硕士、博士学位, 主要从模式识别和机器视觉方面的研究。E-mail:yibeen.wong@gmail.com" ]
- 基金信息：
  
  国家自然科学基金青年基金资助项目(61502396);西南财经大学中央高校基本科研业务费青年教师成长项目(JBK1801076);西南财经大学中央高校基本科研业务费专项资金项目(JBK150503);四川省教育厅一般项目资助(18ZB0484);四川师范大学自制仪器设备项目(ZZYQ2017001);陕西省科技厅工业公关项目(2016GY-088);互联网金融创新及监管四川省协同创新中心资助
- DOI：10.3788/OPE.20182606.1542
  中图分类号： TP391.4;TN216
- 收稿日期：2017-11-09，
  
  录用日期：2017-12-11，
  
  纸质出版日期：2018-06-25
- 稿件说明：
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尹诗白, 孔垂涵, 王一斌. 非监督层次化模糊相关的人体红外图像分割[J]. 光学精密工程, 2018,26(6):1542-1550.

Shi-bai YIN, Chui-han KONG, Yi-bin WANG. Unsupervised hierarchical human target infrared image segmentation through fuzzy correlation[J]. Optics and precision engineering, 2018, 26(6): 1542-1550.
尹诗白, 孔垂涵, 王一斌. 非监督层次化模糊相关的人体红外图像分割[J]. 光学精密工程, 2018,26(6):1542-1550. DOI： 10.3788/OPE.20182606.1542.

Shi-bai YIN, Chui-han KONG, Yi-bin WANG. Unsupervised hierarchical human target infrared image segmentation through fuzzy correlation[J]. Optics and precision engineering, 2018, 26(6): 1542-1550. DOI： 10.3788/OPE.20182606.1542.

摘要

针对当前多级模糊熵算法在分割人体红外图像时，存在划分数需人工指定，全局划分导致熵的信息度量精度受背景干扰，分割精度不高等问题，提出了非监督层次化模糊相关分割。首先采用熵率法将图像划分为若干超像素，确保区域一致性，提高后续处理效率；随后，用准确度量划分适当性的模糊相关来描述图像，构建模糊相关图割2-划分算子，提高层次化分割中单步分割的精度。2-划分算子的核心思想是利用提出的递推计算策略，快速搜索最大模糊相关时目标和背景的划分概率，并用其来设置图割的数据项，实施超像素的模糊相关图割2-划分。最后将2-划分算子与自顶向下的非监督层次化分割策略相结合，迭代地对目标超像素区域实施2-划分，自适应确定划分数，获得人体目标。实验结果表明：较常用算法，该算法不但能自动确定划分数，而且分割精度还提高了约18%，运行时间约为3.8 s，能有效用于人体红外图像分割的工程实践中。

Abstract

The multilevel fuzzy entropy has been adopted as a segmentation method for the human target infrared image. However

it has some problems that the partition number needs to be designated manually

and the global partition manner result in the accuracy of entropy measurement being influenced by background interference and the precision of segmentation being reduced. To address these problems

an unsupervised hierarchical segmentation based on fuzzy correlation strategy was proposed. To ensure the region homogeneity and improve efficiency

the entropy rate method was adoped to segment image into a group of superpixels. Then

the 2-partition segmentation operator through fuzzy correlation was built for improving precision of single step

since the fuzzy correlation can measure the appropriate partition well. The core idea of 2-partition segmentation operator was to utilize an iterative scheme to improve computational efficiency in fuzzy correlation evaluation. Then the probabilities of fuzzy events obtained by maximizing fuzzy correlation were used to define the data terms of graph cut. Finally

the 2-partition segmentation operator was combined with top-down hierarchical segmentation structure. By segmenting the superpixels of object regions by 2-partition segmentation operator iteratively

the partition number could be assigned and human target was achieved in this adaptive approach. The experiment results demonstrate that the proposed method can not only decide the partition number automatically

but can also improve the accuracy by 18% comparing with that of the existing methods and the running time is about 3.8 s. It can be used in engineering practice of human target infrared image segmentation.

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Keywords

references

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