提出一种融合RGB、YCbCr和区域生长的火焰前景提取算法。首先,在YCbCr算法的基础上,从反光和非反光区域考虑R通道和Y通道之间的关系,避免反光和非反光区域中过多噪声对初始分割的干扰;然后,计算连通区域质心权重,自动确定种子点,对完成颜色分割的图像进行区域生长,达到精细分割的目的;最后,全面分析火焰的静态和动态特征,给出面积和周长变异系数及质心运动距离变化比等,进而将火焰与路灯、蜡烛等干扰源区分开。实验结果表明:所提方法克服了单个算法对火焰场景分析中出现的识别精度不高的缺点,同时能识别出反光和非反光区域并快速排除干扰物,减少误判。

In this study, a novel image-processing algorithm to identify flame regions in the foreground based on the combination of the RGB, YcbCr, and seeded region growth (SRG) algorithms is proposed. First, the conventional YCbCr algorithm is improved by incorporating the relationship between the red (R channel) and luminance (Y channel) components. Accordingly, the interfering noise corresponding to the reflective and non-reflective images can be removed. Moreover, in the case of noise-corrupted images, the interference associated with initial image segmentation can be eliminated. By estimating the centroid weight of the connected region, the seed can be automatically determined, resulting in region growth for the color-segmented images, which can facilitate fine segmentation. By analyzing the static and dynamic characteristics of a flame, the variation coefficients of the area and perimeter and the ratio of the centroid movement distance can be calculated. On this basis, a flame region can be distinguished from non-flame regions such as road lamps and candles. The experiment results indicate that the proposed method can not only be used to mitigate deficiencies of the individual algorithms that provide low accuracy, but can also be applied to simultaneously recognize the reflective and non-reflective regions to reduce interference and prevent inaccurate recognition.