Image Processing for Estimating River Grain-Size Distribution
Date Issued
2008
Date
2008
Author(s)
Chung, Chang-Han
Abstract
The measurement techniques of river materials are mainly to get surface grain-size distribution information. There are several traditional measurement techniques, such as volume, grid, and area measurement methods. Among them, the volume measurement method is the most common method used by the Hydraulics, but this method needs a hugeorkload, time and energy. Image analysis techniques have been shown to work well in identifying and measuring particles, consequently they can be powerful tools for measuring the grain size distributions. In thisaper we present a rapid image-processing-based procedure for the measurement of exposed fluvial gravels, defining the steps required to minimize the errors in the derived grain size distribution. The main procedure is divided into four steps: (1)image pre-processing, (2)markeraking, (3)image segmenting, and (4)maximum b-axis measuring. The analyzed stones were obtained from Jingmei River and randomly disposed within a square meter grid in the laboratory and taken picture for the analysis. The measurement errors compared with sieve analyses areuite small in all the cases, consequently we can conclude that the image processing method proposed in this study can efficiently and precisely identify the grain-size distribution and can be used in the follow-upesearch.
Subjects
Image processing
River materials
Artificial neural network
Watershed transform
Type
thesis
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