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Automated Grain Sizing for Fluvial Gravels


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Resources for automated grain sizing for fluvial gravels

The resources on this website are intended to augment the information in our publications on the automated grain sizing (AGS) of fluvial gravels. The site contains information, results and images that could not be included in the publications owing to limitations on available space.

Summary of published work

Graham, Reid & Rice (2005) examines in detail several alternate methods of identifying grain boundaries in digital photographs of exposed gravel surfaces. The performance of each of the techniques is evaluated and recommendations are made about the optimal techniques and associated set of internal parameters. The basis of the evaluation is against manually-digitised grain boundaries. The object-detection algorithm section of the website augments the material in this paper.

Graham, Rice & Reid (2005) describes how the optimal object-detection algorithm may be applied to obtain surface grain-size distributions of fluvial gravels. The precision of the resulting size distributions is evaluated and the consequences of operator decisions on the quality of the size distributions is examined. The basis of the evaluation is against manually collected and graded sediment. The AGS procedure evaluation section of the website augments the material in this paper.

You can download these papers in the publications section of this website.

Software

The algorithms described in our publications have been implemented using Matlab®. We are currently creating a commercial software product for the measurement of grain size in digital photographs. We expect this to be available in October 2005. You can visit the product website at sedimetrics.com. If you would like to receive further information about this software in advance of its publication, please contact Dr David Graham (D.J.Graham@lboro.ac.uk). Unfortunately, we are not able to make the source code available.

Contact information

For further information please contact:

 


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