Segmentation of early fire image of mine based on improved CV model

Because of a great similarity in grayscale value among fire region,fire after glow,and non-fire region inter- ference with high grayscale value in the early mine fire image,it is hard to extract fire region by traditional CV model accurately. To overcome this problem,an improved CV model was proposed to achieve the accurate segmentation of early mine fire image. When calculating the target and background fitting centers,the adaptive weights were introduced to weight fitting centers. It fully considered the grayscale value differences between pixels in each region and fitting centers and the contribution of pixels to calculating the fitting centers was determined according to the grayscale value differences. Therefore,the object and background region fitting centers can be obtained more accurately. In order to ac- celerate the evolution of the proposed model,the median absolute differences of the pixel grayscale values inside and outside the curve are incorporated,which can adaptively adjust the region energy weights inside and outside the curve, instead of original region energy weights,to improve the segmentation efficiency. Finally,the fire region of the early mine fire image is extracted accurately and efficiently. The proposed method was compared to the Otsu algorithm,the CV model,the CV model incorporating energy weight,the CV model incorporating the gradient information and two CV models incorporating similar weights like the proposed method. The extensive experiment results show that the im- proved CV model can gain much a better segmentation accuracy than other methods,and satisfy real-time requirement.