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Proceedings Paper

Cotton trash assessment in radiographic x-ray images with scale-space filtering and stereo analysis
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Paper Abstract

Trash content of raw cotton is a critical quality attribute. Therefore, accurate trash assessment is crucial for evaluating cotton’s processing and market value. Current technologies, including gravimetric and surface scanning methods, suffer from various limitations. Furthermore, worldwide, the most commonly used method is still human grading. One of the best alternatives to the aforementioned approaches is 2D x-ray imaging since it allows a thorough analysis of contaminants in a very precise and quick manner. The segmentation of trash particles in 2D transmission images is difficult since the background cotton is not uniform. Furthermore, there is considerable overlap between the gray levels of trash and cotton. We dealt with this problem by characterizing and identifying the background cotton via scale-space filtering, followed by a “background normalization” process that removes the background cotton, while leaving the trash particles intact. Furthermore, we have successfully employed stereo x-ray vision for recovering the depth information of the piled trash in controlled samples. Finally, the proposed technique was tested on 280 cotton radiographs-with various trash levels-and the results compared favorably to the existing systems of cotton trash evaluation. Given that the approach described here provides the trash mass in real-time, when realized, it will have a wide-spread impact on the cotton industry.

Paper Details

Date Published: 24 February 2005
PDF: 12 pages
Proc. SPIE 5679, Machine Vision Applications in Industrial Inspection XIII, (24 February 2005); doi: 10.1117/12.587334
Show Author Affiliations
Mehmet Serdar Dogan
Hamed Sari-Sarraf, Texas Tech Univ. (United States)
Eric Francois Hequet, Texas Tech Univ. (United States)


Published in SPIE Proceedings Vol. 5679:
Machine Vision Applications in Industrial Inspection XIII
Jeffery R. Price; Fabrice Meriaudeau, Editor(s)

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