Urban Road Image Segmentation Algorithm Based on Statistical Information

Abstract

Urban road image segmentation is an important technology of intelligent city management and pilotless driving. In order to improve the effect of image segmentation, direct at the deficiency of single seed point and fixed threshold of traditional region growing algorithm, a seed selection method based on the gray level of two-dimensional histogram and local variance is proposed, and the dynamic threshold is used to change the region growing rule. The experimental results show that the seeds selected by this method can be highly representative, and realize the complete segmentation of the image. Based on the dynamic threshold region growth rule, the image segmentation has a better effect.