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24 October 2017 Classification and quality evaluation of ginned cotton based on color image fusion technique
Zhi-Yong Chen, Xiao-Hui Li, Bin Xiao, Zhifeng Zhang, Yu-Rong Li, Li-Jie Geng, Yu-Sheng Zhai, Yong-You Han
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Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 104621R (2017) https://doi.org/10.1117/12.2284166
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Abstract
Ginned cotton’s quality is one significant factor to evaluate the cotton grade and influence the yarn qualities. Ginned cotton is always mixed with contaminants during picking, storing, drying, transporting, purchasing, and processing. Manual evaluation is time consuming, labor intensive, and unreliable. This paper proposed a fast feature extraction algorithm is presented for the measurement of cotton defects in ginned cotton within a complex background. The edge of cotton defects are extracted from fusion of three channel image of color image. A criterion based on areas is proposed to achieve fast morphological analysis. The different defects can be inspected automatically based on different thresholds. The comparison experiments between measuring system and technician were done and analyzed. The costing time of measuring system was less than 30 seconds, and accuracy was 89.5%. The measuring results show the method can meet with the requirement of grade determination of ginned cottons.
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Zhi-Yong Chen, Xiao-Hui Li, Bin Xiao, Zhifeng Zhang, Yu-Rong Li, Li-Jie Geng, Yu-Sheng Zhai, and Yong-You Han "Classification and quality evaluation of ginned cotton based on color image fusion technique", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104621R (24 October 2017); https://doi.org/10.1117/12.2284166
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KEYWORDS
Computer vision technology
Image analysis
Image classification
Image fusion
Image quality
Information fusion
Machine vision
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