ISSN:
1013-9826
Source:
Scientific.Net: Materials Science & Technology / Trans Tech Publications Archiv 1984-2008
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
A real-time white ginseng quality evaluation system based on a machine visiontechnique and artificial neural networks was developed to replace the current manual grading andits efficiency was tested. The system consisted of conveyor, image acquisition systemsynchronized with a sample-detecting sensor, and image processing and decision-making system.Software running under Windows system was developed. The algorithm included threeconsecutive stages of (a) image acquisition and preprocessing, (b) mathematical feature extraction,and (c) grade decision using artificial neural networks. Mathematical features such as area ratio,mean and standard deviation of gray level, skewness of gray level histogram, and the number ofrun segment, were extracted from five equally divided parts of a specimen. An artificial neuralnetwork model was used to classify samples into three grading categories. The grading error of thesystem was about 26%, which is comparable to the 30% in case of manual grading. The gradingrate was one sample per a second
Type of Medium:
Electronic Resource
URL:
http://www.tib-hannover.de/fulltexts/2011/0528/01/51/transtech_doi~10.4028%252Fwww.scientific.net%252FKEM.321-323.1225.pdf
