基于斜分倒数交叉熵和蜂群优化的火焰图像阈值选取

吴一全; 孟天亮; 王凯

doi:10.3788/OPE.20142201.0235

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基于斜分倒数交叉熵和蜂群优化的火焰图像阈值选取

信息科学 | 更新时间：2020-08-13

- 基于斜分倒数交叉熵和蜂群优化的火焰图像阈值选取
- Threshold selection of flame image based on reciprocal cross entropy and bee colony optimization
- 光学精密工程 2014年22卷第1期页码：235-243
- 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：
  
  江苏高校优势学科建设工程资助项目( )
- DOI：10.3788/OPE.20142201.0235
  中图分类号： TP391.4
- 收稿日期：2013-05-31，
  
  纸质出版日期：2014-01-15
- 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

李树涛, 王耀南. 基于神经网络的回转窑火焰图像分割[J]. 仪器仪表学报, 2001, 22(1): 10-12, 16.

LI SH T, WANG Y N. The segmentation of kiln flame image based on neural networks [J]. Chinese Journal of Scientific Instrument, 2001, 22(1): 10-12, 16.(in Chinese)

易正明, 吕子剑, 刘志明. 氧化铝回转窑火焰图像处理与特征提取[J]. 仪器仪表学报, 2006, 27(8): 969-972.

YI ZH M, LV ZH J, LIU ZH M. Flame image processing and its characteristic extraction for alumina rotary kiln [J]. Chinese Journal of Scientific Instrument, 2006, 27(8): 969-972.(in Chinese)

孙鹏, 周晓杰, 柴天佑. 基于纹理粗糙度的回转窑火焰图像FCM分割方法[J]. 系统仿真学报, 2008, 20(16):4438-4442.

SUN P, ZHOU X J, CHAI T Y. FCM segmentation for flame image of rotary kiln based on texture coarseness [J]. Journal of System Simulation, 2008, 20(16): 4438-4442. (in Chinese)

杨永敏, 樊继壮, 赵杰. 基于超熵和模糊集理论的带钢表面缺陷分割[J]. 光学精密工程, 2011, 19(7): 1651-1658.

YANG Y M, FAN J ZH, ZHAO J. Steel strip surface defect segmentation based on excess entropy and fuzzy set theory [J]. Opt. Precision Eng., 2011, 19(7): 1651-1658. (in Chinese)

何志勇, 孙立宁, 黄伟国, 等. 基于Otsu准则和直线截距直方图的阈值分割[J]. 光学精密工程, 2012, 20(10):2315-2323.

HE ZH Y, SUN L N, HUANG W G, et al.. Thresholding segmentation algorithm based on Otsu criterion and line intercept histogram [J]. Opt. Precision Eng., 2012, 20(10):2315-2323.(in Chinese)

靳永亮, 王延杰, 刘艳滢, 等. 红外弱小目标的分割预检测[J]. 光学精密工程, 2012, 20(1): 171-178.

JIN Y L, WANG Y J, LIU Y Y, et al.. Pre-detection method for small infrared target [J]. Opt. Precision Eng., 2012, 20(1):171-178.(in Chinese)

KAPUR J N, SAHOO P K, WONG A K C. A new method for gray-level picture thresholding using the entropy of histogram [J]. Computer Vision, Graphics and Image Processing, 1985, 29(1): 273-285.

ABUTALEB A S. Automatic thresholding of gray-level picture using two-dimensional entropies [J]. Pattern Recognition, 1989, 47(1):22-32.

BRINK A D. Thresholding of digital image using two-dimensional entropies [J]. Pattern Recognition, 1992, 25(8):803-808.

DU F , SHI W K, CHEN L Z, et al.. Infrared image segmentation with 2D maximum entropy method based on particle swarm optimization [J]. Pattern Recognition Letters, 2005, 26(5): 597-603.

LI C H, LEE C K. Minimum cross entropy thresholding [J]. Pattern Recognition, 1993, 26(4):617-625.

BRINK A D, PENDOCK N E. Minimum cross-entropy threshold selection [J]. Pattern Recognition, 1996, 29(1):179-189.

TANG K Z, YUAN X J, SUN T K, et al.. An improved scheme for minimum cross entropy threshold selection based on genetic algorithm [J]. Knowledge-Based System, 2011, 24(8): 1131-1138.

吴一全, 张晓杰, 吴诗婳. 基于混沌弹性粒子群优化与基于分解的二维交叉熵阈值分割[J]. 上海交通大学学报, 2011, 45(3):301-307.

WU Y Q, ZHANG X J, WU SH H. Two-dimensional cross entropy thresholding based on chaotic resilient particle swarm optimization or decomposition [J]. Journal of Shanghai Jiaotong University, 2011, 45(3):301-307. (in Chinese)

乔韡韡, 吴成茂. 二维最大类间交叉熵阈值分割法[J]. 西北大学学报:自然科学版, 2008, 38(3):374-378.

QIAO W W, WU CH M. Two-dimensional thresholding segmentation method based on maximum inter-class cross entropy [J]. Journal of Northwest University:Natural Science Edition, 2008, 38(3): 374-378. (in Chinese)

雷博, 范久伦. 灰度图像的二维交叉熵阈值分割法[J].光子学报, 2009, 38(6):1572-1576.

LEI B, FAN J L. Two-dimensional cross-entropy thresholding segmentation method for gray-level images [J]. Acta Photonica Sinica, 2009, 38(6):1572-1576. (in Chinese)

PAL S K, PAL N R. Entropic thresholding [J]. Signal Processing, 1989, 16(2):97-108.

吴一全, 占必超. 基于混沌粒子群优化的倒数熵阈值选取方法[J]. 信号处理, 2010, 26(7): 1044-1049.

WU Y Q, ZHAN B CH. Thresholding based on reciprocal entropy and chaotic particle swarm optimization [J]. Signal Processing, 2010, 26(7): 1044-1049. (in Chinese)

吴一全, 潘喆, 吴文怡. 二维直方图区域斜分的最大熵阈值分割算法[J]. 模式识别与人工智能, 2009, 22(1): 162-168.

WU Y Q, PAN ZH, WU W Y. Maximum entropy image thresholding based on two-dimensional histogram oblique segmentation [J]. Pattern Recognition and Artificial Intelligence, 2009, 22(1): 162-168.(in Chinese)

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