Research on image foreground target extraction algorithm under uneven illumination

In order to solve the problem that the image binarization segmentation under uneven illumination conditions can not be balanced in terms of processing speed and effect, this paper proposes a method of background estimation and threshold segmentation for images under uneven illumination conditions by using the second moving average method. This method uses the quadratic moving average prediction method to predict the trend of the gray value of each row of the image, so as to obtain the mutation point of the foreground target and the background to supplement the background image under the missing foreground target, and then uses the difference method to separate the foreground target and the background, and extract the target image for recognition. Experimental results show that the algorithm designed in this paper solves the phenomenon that the smaller neighborhood value of the local threshold algorithm will cause the hollowing out of the large foreground image to a certain extent, and increases the image processing rate to 140ms / frame, which can effectively improve the image background processing effect and accelerate the rate of image segmentation. It can basically be applied to the real-time detection scene of image anti-light interference under certain conditions.