Using multiple superpixel segmentation and merging of digital image method to auto-estimate gravel grain size

Abstract

The study of grain size distribution of gravels is an important and challenging issue in stratigraphy and morphology, especially in the field of automated measurement. It largely reduces many manual processes and time consumption. Using digital image processing method to extract effective information from digital images is a more advanced method. In this study, a digital image method using automated multiple superpixel segmentation and merging is proposed for extraction of the grain size data. It adds grain size estimation and improved merging rules and repeated execution rules to the superpixel segmentation and merging. It has made great improvements in many respects, especially in the accuracy of edge segmentation and measurement. Compared with manual measurement and other image processing ways, the method proposed in this paper is an efficient approach for precisely measuring the grain size distribution of riverbed material.