摘要
针对红外图像的火焰识别,采用基于粒子群优化算法的二维最大熵阈值选取方法,选取最佳阈值对红外图像进行分割,使可疑区域从背景中分离出来.选择物体的高度作为特征量,采用标准模板序列,设计两层模糊分类器分析物体的高度变化和灰度分布,给出可疑目标隶属于火焰的评价.实验证明,这种结合火焰动、静特性的算法鲁棒性强,识别率及灵敏度较高,适用于广范围的火灾监控.
Flame detection and recognition is a kernel issue in the automatic fire surveillance system. An effective algorithm was put forward to recognize flame object in infrared video sequence. Otsu thresholding based on particle swarm optimization algorithm is used for segmentation of the image, after that the fire colored objects will be picked up. Next, a double-deck fuzzy system is employed to analyze the difference of objects＇ height and gray scale distribution, and then the membership degree is given to the flame. The experimental results prove that the method can effectively extract flame object from complex background.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2008年第12期1979-1982,1987,共5页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金资助项目(60774052)
关键词
红外图像
两层模糊分类器
火焰识别
粒子群优化算法
infrared image
double-deck fuzzy system
flame detection
particle swarm optimization
