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

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Document pages: 8 pages

Abstract: The study of the grain-size distribution of gravelsis always an important and challenging issue in stratigraphy and morphology,especially in the field of automated measurement. It largely reduces manymanual processes and time consumption. Precise segmentation method plays a veryimportant role in it. In this study, a digital image method using an improvednormalized cuts algorithm is proposed for auto-segmentation of gravel image. Itadded grain-size estimation, and used the featurevector based on color. It has made great improvements in many respects,especially in accuracy of edge segmentation and automation. Compared withmanual measurement methods and other image processing methods, the methodstudied in this paper is an efficient method for precisely segmenting gravelimages.

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