摘要
在传统的火焰检测算法中,火焰前景提取容易出现火焰轮廓不完整和抗干扰性较差的情况。为此,融合红/绿/蓝(RGB)、色调/饱和度/亮度(HSI)和最大类间方差法(Otsu)提出一种新的火焰前景提取算法,利用双颜色空间融合的算法能够提取较完整的火焰轮廓,使火焰轮廓所受干扰影响程度尽量小。获得前景图像后用灰度共生矩阵提取纹理特征,在YCbCr颜色空间中提取颜色特征,用于最终的火焰判断。同时提出一种改进的概率神经网络(PNN),将传统PNN中单一固定值的平滑因子改进为多变量参数,用条件期望最大化(ECM)算法对PNN中平滑因子进行参数优化,再将提取的特征输入改进后的PNN中训练测试。仿真结果表明,该算法具有良好的抗干扰能力,能够提高对火焰识别的精度。
The traditional flame detection algorithm often achieves incomplete contour and poor anti-interference performance in the process of flame foreground extraction.This paper proposes a new flame foreground extraction algorithm,which combines RGB,HSI,and Ostu(maximum between-cluster variance method).The developed algorithm can extract flame contour completely and eliminate the smallest possible interference.Then,static features such as textures and colors in YCbCr are extracted by using a co-occurrence matrix and used for final flame judgment.Finally,an improved probabilistic neural network(PNN)method is developed to adjust the traditional smoothing factor from a single fixed value to a parameter that contains multi-variables,after which the expectation/conditional maximization(ECM)algorithm is used to find the optimal parameters.The extracted features are input in the advanced PNN and used for the training test.Simulation results show that the proposed algorithm can improve the accuracy of flame identification with good anti-interference performance.
作者
谭勇
谢林柏
冯宏伟
彭力
张正道
Tan Yong;Xie Linbo;Feng Hongwei;Peng Li;Zhang Zhengdao(School of Internet of Things Engineering,Jiangnan University,Wuxi,Jiangsu21A122,China;Wuxi Institute of Technology,Wuxi,Jiangsu 214122 China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第16期107-113,共7页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61374047,61873112)
教育部中国移动科研基金(MCM20170204)
关键词
图像处理
火焰检测
前景提取
最大类间方差法
概率神经网络
条件期望最大化
image processing
flame detection
foreground extraction
maximum betw een-cluster variance m ethod
probabilistic neural netw ork
expectation/conditional maximization
