Threshold selection of flame image based on reciprocal cross entropy and bee colony optimization

WU Yi-quan; MENG Tian-liang; WANG Kai

doi:10.3788/OPE.20142201.0235

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Threshold selection of flame image based on reciprocal cross entropy and bee colony optimization

Information Sciences | 更新时间：2020-08-13

- Threshold selection of flame image based on reciprocal cross entropy and bee colony optimization
- Optics and Precision Engineering Vol. 22, Issue 1, Pages: 235-243(2014)
- 作者机构：
  
  1. 南京航空航天大学电子信息工程学院,江苏南京,210016
  2. 华中科技大学煤燃烧国家重点实验室,湖北武汉,430074
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142201.0235
  CLC： TP391.4
- Received：31 May 2013，
  
  Published：15 January 2014
- 稿件说明：
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吴一全, 孟天亮, 王凯. 基于斜分倒数交叉熵和蜂群优化的火焰图像阈值选取[J]. 光学精密工程, 2014,22(1): 235-243

WU Yi-quan, MENG Tian-liang, WANG Kai. Threshold selection of flame image based on reciprocal cross entropy and bee colony optimization[J]. Editorial Office of Optics and Precision Engineering, 2014,22(1): 235-243
吴一全, 孟天亮, 王凯. 基于斜分倒数交叉熵和蜂群优化的火焰图像阈值选取[J]. 光学精密工程, 2014,22(1): 235-243 DOI： 10.3788/OPE.20142201.0235.

WU Yi-quan, MENG Tian-liang, WANG Kai. Threshold selection of flame image based on reciprocal cross entropy and bee colony optimization[J]. Editorial Office of Optics and Precision Engineering, 2014,22(1): 235-243 DOI： 10.3788/OPE.20142201.0235.

摘要

提出了基于斜分倒数交叉熵和蜂群优化的火焰图像阈值选取方法以便更为准确地分割火焰图像。以最小倒数交叉熵作为阈值选取准则

解决了 Shannon 熵定义中存在的无意义值问题。同时

以二维直方图斜分方式更加准确地划分目标和背景

提高了算法抗噪性能

且使需要选取的阈值个数由两个变为一个

减少了算法运行时间。此外

采用蜂群优化算法加速对最佳阈值的搜索

使速度提升了约80%~140%

进一步提高了算法的实时性。最后

针对火焰图像进行了大量实验

并与二维斜分最大 Shannon 熵法、基于混沌小生境粒子群优化(NCPSO)的二维斜分最大倒数熵法进行了比较。结果表明

提出的方法在分割效果上优势明显

且抗噪性能更好

是一种实时有效的火焰图像分割方法。

Abstract

A flame image segmentation method was proposed based on reciprocal cross entropy threshold selection and bee colony optimization to improve the segmented accuracy. By using the minimum reciprocal cross entropy as the threshold selection criteria

the drawback of an undefined value at zero in Shannon entropy definition was avoided. At the same time

the 2D histogram oblique segmentation was taken to partition the object and background precisely to improve the anti-noise performance. By which

only one threshold instead of two thresholds needs to be searched

and the running time is reduced. In addition

the bee colony optimization was applied to acceleration of the process to find the optimal threshold to further improve the real-time performance of this algorithm and increase the algorithmic speed by 80%-140%. Finally

a large number of experiments on flame images were processed and then the experimental results were compared with the maximum Shannon entropy method based on 2D histogram oblique segmentation and the maximum reciprocal entropy method based on 2D histogram oblique segmentation and Niche Chaotic Mutation Particle Swarm Optimization (NCPSO). The obtained results show that the proposed method has obvious advantages in segmentation effects and has better anti-noise ability and real-time performance for flame images.

关键词

Keywords

references

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