摘要
本文提出了一种早期油料火灾图像检测及识别算法。将火焰颜色、亮度及运动特征作为火灾检测与识别的判据,在火焰颜色模型和运动图像差分模型的基础上提出利用离散分形布朗随机增量场模型对早期油料火灾图像进行进一步的判定。模拟坑道实验结果表明,该算法能够有效提高油料火灾检测与识别的准确率,降低误报、漏报率。
An algorithm of early oil fire image detection and recognition is put forward. The flame color, brightness and movement characteristics are chosen as the criteria. The early oil fire images are further detected and recognized by the algorithm of the Discrete Fraetal Brownian Incremental Random Field model based on an analysis of the flame model and the differential model. The results of the simulated tunnel experiments show that the algorithm can successfully detect and recognize oil fire.
出处
《计算机工程与科学》
CSCD
北大核心
2010年第2期72-74,共3页
Computer Engineering & Science
关键词
油料火灾图像
火焰模型
差分模型
离散分形布朗随机增量场模型
oil fire image
flame model
differential model
discrete fractal brownian incremental random field model （DFBIR）
