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Proceedings Paper

A method to improve sugar crystals classification
Author(s): Jules-Raymond Tapamo; Yuren Deokaran
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Paper Abstract

There has been a long development of sugar crystal analysis techniques. Initially crystals were manually passed through various increasingly finer sieves so that one could manually calculate what percentage of crystals and crystal masses lay in various size groups. Later microscopes were used on small samples to take pictures of crystals so that they could be sized manually at higher degree of accuracy. In order to increase the accuracy, image processing are being used to analyze the pictures taken under microscope. The main concern is to analyze crystals with width greater than 50 micrometers. The ideal crystal is roughly square and has a width of approximately 120 micrometers. There is then a need to separate crystals into two main classes: the class of crystals that have to be considered for the analysis and those that will be rejected. This classification process involves: the enhancement of the quality of the image, the binarization of the image, the extraction of the connected components, the features extraction from each connected component and the characterization of the classes. During this process, there is more often a lost of information and in some case an intrusion of noise. These can have as result some misclassifications. These misclassifications can be caused by touching crystals or overlapping crystals that are treated as single crystal. These can also be due to the fact that edges of crystals are not well extracted. In this paper we present a method to alleviate those misclassifications using mathematical morphology and a combination of binarization and edge detection. This method gives better classification. Some results are presented.

Paper Details

Date Published: 3 May 2004
PDF: 8 pages
Proc. SPIE 5303, Machine Vision Applications in Industrial Inspection XII, (3 May 2004); doi: 10.1117/12.531166
Show Author Affiliations
Jules-Raymond Tapamo, Univ. of Natal (South Africa)
Yuren Deokaran, Univ. of Natal (South Africa)

Published in SPIE Proceedings Vol. 5303:
Machine Vision Applications in Industrial Inspection XII
Jeffery R. Price; Fabrice Meriaudeau, Editor(s)

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