Segmentation and classification of four common cotton contaminants in x-ray microtomographic images

Sri-Kaushik Pavani; Mehmet Serdar Dogan; Hamed Sari-Sarraf; Eric Francois Hequet

doi:10.1117/12.527117

3 May 2004 Segmentation and classification of four common cotton contaminants in x-ray microtomographic images

Sri-Kaushik Pavani, Mehmet Serdar Dogan, Hamed Sari-Sarraf, Eric Francois Hequet

Proceedings Volume 5303, Machine Vision Applications in Industrial Inspection XII; (2004) https://doi.org/10.1117/12.527117
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

Technologies currently used for cotton contaminant assessment suffer from some fundamental limitations. These limitations result in the misassessment of cotton quality and may have a serious impact on the evaluation of the economic value of the cotton crop. This paper reports on the recent advances in the use of a 3D x-ray microtomographic system that employs image processing and pattern recognition techniques to accurately detect and classify trash present in cotton. The proposed method offers an attractive alternative to existing trash evaluation technologies, because of its ability to produce 3D representations of the samples, to robustly segment the trash from its background, and to accurately classify the contaminant types. This procedure could have a serious impact on the process control technologies (cotton lint cleaning), and indeed on the economic value of cotton.

Citation Download Citation

Sri-Kaushik Pavani, Mehmet Serdar Dogan, Hamed Sari-Sarraf, and Eric Francois Hequet "Segmentation and classification of four common cotton contaminants in x-ray microtomographic images", Proc. SPIE 5303, Machine Vision Applications in Industrial Inspection XII, (3 May 2004); https://doi.org/10.1117/12.527117

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