基于差分盒维数的彩色烟雾图像识别

提出了利用差分盒维数与颜色特征相结合的图像识别方法来将彩色烟雾图像从森林背景中识别出来。该方法首先用差分盒维数算法来计算整幅图像的分形维数值并基于该值对图像进行分割,再以RGB空间的烟雾颜色特征为依据,对差分盒维数方法分割出的区域进行判别,识别出烟雾区域。为改善算法的计算精度,提高算法运算速度,提出了减少子窗口内盒子的覆盖数量、改变子窗口内灰度等级的改进算法。仿真实验结果表明,基于改进的差分盒维数方法,不仅运算速度提高近50％,而且能够更好地反映图像表面的纹理信息。再结合颜色特征能从森林背景中准确的识别出烟雾。该方法可用于森林火灾的预警。

Color smoke image recognition based on differential box-counting fractal dimension algorithm

The smoke recognition method was proposed which used the differential box-counting fractal dimension combined with the color feature, in order to effectively identify the smoke from the forest background in color image. First, differential box dimension algorithm was used to calculate the fractal dimension value of the whole image. Second, based on the value of the image segmentation and smoke color characteristics in RGB color space, smoke region was recognized according to the split of differential box counting method regional discrimination. The algorithm reduced the number of sub-window within the coverage of the box to improve the accuracy of algorithm calculation and the speed of arithmetic operation. The changed gradation within the sub-window was proposed. The results showed that the improved differential box counting method, combined with color feature technique could accurately identify the smoke. The surface texture information was better reflected in the image and the speed of calculation is increased by nearly 50％.This method can be used for early warning of forest fires.