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Portable Image Analysis System for Characterizing Aggregate Morphology
Author(s) -
Linbing Wang,
D. Stephen Lane,
Yang Lu,
Cristian Druta
Publication year - 2009
Publication title -
transportation research record journal of the transportation research board
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.624
H-Index - 119
eISSN - 2169-4052
pISSN - 0361-1981
DOI - 10.3141/2104-01
Subject(s) - aggregate (composite) , laptop , computer science , image processing , process (computing) , digital image , macro , digital image processing , digital camera , orientation (vector space) , software , range (aeronautics) , computer graphics (images) , computer vision , artificial intelligence , image (mathematics) , mathematics , engineering , geometry , materials science , composite material , aerospace engineering , programming language , operating system
In the past decade, the application of image-based evaluation of particle shape, angularity, and texture has been widely researched to characterize aggregate morphology. Yet the lack of rapid, objective, and quantitative methods for assessment has inhibited its application in the engineering process. However, recent advances in technology have produced pocket computers with as much processing power as some desktop computers. This project takes advantage of these advances to develop an inexpensive portable image analysis system for characterizing aggregate morphology. The system uses an integral pocket computer–high resolution camera but can employ individual components consisting of a digital camera and a laptop or desktop computer. Digital images of coarse aggregate particles are captured with the camera and analyzed within the Matlab software program environment with a macro developed and written for this project to characterize particle morphology with respect to three parameters (shape, angularity, and texture) on the basis of the particle perimeter (outline or edge). For this purpose, several coarse aggregate types from 10 various Virginia sources were analyzed, and the reliability of the image processing was statistically assessed. Asymptotic analysis was performed to determine the number of images needed to obtain a statistically stable value for each aggregate parameter. It was determined that images acquired at close range (2 or 3 in.) were needed to provide sufficient resolution to adequately characterize the aggregates. Also, it was found that statistically valid values for the three aggregate parameters could be obtained from 15 particle images of random but stable orientation, thus making the system efficient in characterizing coarse aggregate morphology.

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