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. 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.
WU Yi-quan, MENG Tian-liang, WANG Kai. Threshold selection of flame image based on reciprocal cross entropy and bee colony optimization[J]. Optics and Precision Engineering, 2014, 22(1): 235.
[1] 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)
[2] 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)
[3] 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)
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] ABUTALEB A S. Automatic thresholding of gray-level picture using two-dimensional entropies [J]. Pattern Recognition, 1989, 47(1): 22-32.
[9] BRINK A D. Thresholding of digital image using two-dimensional entropies [J]. Pattern Recognition, 1992, 25(8): 803-808.
[10] 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.
[11] LI C H, LEE C K. Minimum cross entropy thresholding [J]. Pattern Recognition, 1993,26(4): 617-625.
[12] BRINK A D, PENDOCK N E. Minimum cross-entropy threshold selection [J]. Pattern Recognition,1996, 29(1): 179-189.
[13] 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.
[14] 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)
[15] 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)
[16] 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.
[17] PAL S K, PAL N R. Entropic thresholding [J]. Signal Processing, 1989,16(2): 97-108.
[18] 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)
[19] 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)
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. WU Yi-quan, MENG Tian-liang, WANG Kai. Threshold selection of flame image based on reciprocal cross entropy and bee colony optimization[J]. Optics and Precision Engineering, 2014, 22(1): 235.