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

Sri Kaushik Pavani, Mehmet S. Dogan, Hamed Sari-Sarraf, Eric F. Hequet

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


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.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2004
EventMachine Vision Applications in Industrial Inspection XII - San Jose, CA, United States
Duration: Jan 21 2004Jan 22 2004


  • Background normalization
  • Computer vision
  • Cotton contamination
  • Delaunay triangulation
  • Fuzzy logic
  • X-ray tomography


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