融合独立分量分析和视觉显著性的烟雾分割检测算法  

Smoke Detection Segmentation Algorithm with the Fusion of ICA and GBVS

作　　者：张静[1] 李鸿燕[1] 

机构地区：[1]太原理工大学数字音视频技术研究中心,晋中030600

出　　处：《科学技术与工程》2017年第25期246-250,共5页Science Technology and Engineering

摘　　要：在烟雾前景分离中,针对传统高斯混合模型分离的前景仍存在大量噪声点的问题,将独立分量分析(ICA)应用于分离烟雾前景,对传统烟雾前景分离算法进行改进。算法通过ICA消除烟雾前景和背景间的二阶和高阶相关,降低非烟雾成分的干扰;并通过基于图的视觉显著性(GBVS)来缩减预判的烟雾前景区域,得到较为纯净的烟雾区域。实验结果表明,与基于混合高斯模型的烟雾检测算法相比,该算法提取的烟雾区域小而集中,主观视觉评价以及客观指标均显示算法的识别效果更优。In the smoke foreground separation process,in view of a lot of noise still produced in the traditional Gaussian mixture model to separate the foreground independent component analysis（ ICA） was applied to separate the smoke foreground and improved traditional smoke separation algorithm. The algorithm with the ICA to eliminate the second order and higher order related between foreground and background of smoke,and reduce the interference of non-smoke composition. The smoke foreground area above was reduced through the graph based visual saliency（ GBVS） to be a pure smoke area. The experimental results show that compared with cigarette-smoke detection based on Gaussian mixture model and frame difference,the smoke area extracted is smaller and more concentrated.Subjective visual evaluation and objective indicators both shows better recognition effect of the algorithm.

关 键 词：烟雾检测 独立分量分析(ICA) 基于图的视觉显著性(GBVS) 

分 类 号：TN911.73[电子电信—通信与信息系统]