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.