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has been cited by the following article:

• TITLE: Gravel Image Auto-Segmentation Based on an Improved Normalized Cuts Algorithm

  AUTHORS: Chao Wang, Xiangliang Lin, Changsheng Chen

  KEYWORDS: Normalized Cuts, Gravel Image, Auto-Segmentation

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.7 No.3, March 25, 2019

  ABSTRACT: The study of the grain-size distribution of gravels is always 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. Precise segmentation method plays a very important role in it. In this study, a digital image method using an improved normalized cuts algorithm is proposed for auto-segmentation of gravel image. It added grain-size estimation, and used the feature vector based on color. It has made great improvements in many respects, especially in accuracy of edge segmentation and automation. Compared with manual measurement methods and other image processing methods, the method studied in this paper is an efficient method for precisely segmenting gravel images.