光谱学与光谱分析 |
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A Dual-Band Algorithm to Detect Contaminants with Low Visibility on Chicken Carcass Surface |
WU Wei1, CHEN Gui-yun1, XIA Jian-chun2,YE Chang-wen1, CHEN Kun-jie1* |
1. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China 2. Changzhou Textile Garment Institute of Technology, Changzhou 213164, China |
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Abstract A novel dual-band algorithm for detecting contaminants with low visibility on chicken carcass surface based on hyperspectral image was proposed. Firstly, The 675 nm band image, in which the identity of the intensity within ROI (Region of Interest)is the best and the spectrum difference between ROI and the edge of the ROI is the biggest, was chosen from the hyperspectral data for binarization and the mask was extracted by using region growing on the biggest connected area. Then the “and” operation between the mask and the 400 nm band image with the largest discriminability of contaminants was carried out. The max ROI which can self adapt according to the position and shape of the chicken carcass was obtained. Finally,the labeling method was used to recognize if there are contaminations within the segmented ROI. The results showed that through the proposed method, the max ROIs which could self adapt to the position and shape of the chicken carcass were extracted and the average size of the ROI was bigger than 176% compared to that by existing methods. The average correct identification rate of contaminations such as blood, bile and feces was 81.6%.
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Received: 2014-07-19
Accepted: 2014-10-16
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Corresponding Authors:
CHEN Kun-jie
E-mail: kunjiechen@njau.edu.cn
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[1] Musgrove M T, Berrang M E, Byrd J A, et al. Poultry Science, 2001, 80(6): 825. [2] Berrang M E, Buhr R J, Cason J A. Poultry Science, 2000, 79(2): 286. [3] Berrang M E, Smith D P, Windham W R, et al. Journal of Food Protection, 2004, 67(2): 235. [4] Everard C D, Kim M S, Lee H. Proc. SPIE, Sensing for Agriculture and Food Quality and Safety VI, 2014, 9108: 91080A. [5] Park B, Chen Y R. Transactions of the ASAE, 1994, 37(6): 1983. [6] Park B, Chen Y R, Huffman R W. Journal of Food Engineering, 1996, 30(1): 197. [7] Park B, Chen Y R, Nguyen M. Journal of Agricultural Engineering Research, 1998, 69(4): 351. [8] Yoon S C, Park B, Lawrence K C, et al. Computers and Electronics in Agriculture, 2011, 79(2): 159. [9] Park B, Lawrence K C, Windham W R, et al. Transactions of the ASAE, 2002, 45(6): 2017. [10] Park B, Yoon S C, Windham W R, et al. Sensing and Instrumentation for Journal of Food Quality and Safety, 2011, 5(1): 25. [11] ZHAO Jin-hui,TU Dong-cheng,OUYANG Jing-yi, et al(赵进辉, 涂冬成, 欧阳静怡, 等). Acta Agriculturae Universitatis Jiangxiensis(江西农业大学学报), 2011, 33(3): 0573. [12] ZHAO Jin-hui, YU Fang, WU Rui-mei, et al(赵进辉, 吁 芳, 吴瑞梅, 等). Laser &Optoelectronics Progress(激光与光电子学进展), 2011, 48(7): 163. [13] Yang C C, Chao K, Kim M S. Sensing and Instrumentation for Food Quality and Safety, 2009, 3(1): 70. [14] Chao K, Yang C C, Chen Y R, et al. Poultry Science,2007, 86(11): 2450. [15] Yang C C, Chan D E, Chao K, et al. Proc. SPIE, Optics for Natural Resources, Agriculture and Foods, 2006, 6381: 63810Y. |
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