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• W2033112230 abstract "Natural bed topography and habitat is affected by the composition of gravels in various shapes and sizes. Traditional measurement methods for grain size distribution are time-consuming and labor-intensive. Recent advances in image processing techniques facilitate automated grain size measurement through digital images. This study introduces a refined automated grain sizing method (R-AGS) incorporating a neural fuzzy network for automatically estimating the grain size distribution, specifically for digital images composed of grains ranging from 16 mm to 512 mm. A total of 130 digital images captured from the Lanyang river-bed in northeast Taiwan are used to assess the R-AGS performance. We demonstrate the neural fuzzy network can adequately identify the binary threshold, which is a crucial parameter of the AGS procedure, and the proposed R-AGS can be intelligibly used for automated accurate estimation of grain size distribution with much less labor-intensiveness for each digital image. Moreover, it is easy to re-construct the network by updating rule nodes for image samples significantly different from this study; consequently its applicability and practicability could be expanded." @default.
• W2033112230 created "2016-06-24" @default.
• W2033112230 creator A5037875355 @default.
• W2033112230 creator A5089700077 @default.
• W2033112230 date "2013-04-01" @default.
• W2033112230 modified "2023-09-23" @default.
• W2033112230 title "A refined automated grain sizing method for estimating river-bed grain size distribution of digital images" @default.
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• W2033112230 doi "https://doi.org/10.1016/j.jhydrol.2013.01.026" @default.
• W2033112230 hasPublicationYear "2013" @default.
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