激光与光电子学进展, 2011, 48 (7): 073001, 网络出版: 2011-06-02   

基于分段主成分分析与波段比的鸡胴体表面粪便污染物检测

Detection of Fecal Contaminants on Chicken Carcasses Using Segmented Principal Component Analysis and Band Ratio Algorithm
作者单位
江西农业大学工学院, 江西 南昌 330045
摘要
以鸡胴体为研究对象,应用高光谱图像技术结合分段主成分分析和波段比等数据处理方法来检测鸡胴体表面粪便污染物。首先采集400\~1000 nm的鸡胴体表面高光谱图像;然后应用分段主成分分析获得7个特征波长(520.64, 542.12, 561.61, 577.04, 595.6, 703.82和852.1 nm),并以577.04/520.64 nm波段比图像和852.1/703.82 nm波段比图像进行一次波段加运算后的图像作为检测鸡胴体表面粪便污染物的特征图像;最后运用阈值分割和数学形态学完成粪便污染物的提取。实验结果表明,对60个鸡胴体样本进行检测,盲肠、直肠和十二指肠粪便污染物检测正确率分别为100%,100%和96%,检测总正确率为93.3%。
Abstract
Using chicken carcasses as the research subject, fecal contaminants on chicken carcasses are detected by using hyperspectral imaging technology and combining segmented principal component analysis and band ratio algorithm. Firstly, hyperspectral images of chicken carcasses from 400 to 1000 nm are collected. Secondly, seven characteristic wavelengths (520.64,542.12,561.61,577.04,703.82,595.6 and 852.1 nm) are obtained by segmented principal component analysis, and the images obtained using 577.04/520.64 nm band ratio image added by 852.1/703.82 nm band ratio image are selected as the characteristic images of the detection of fecal contaminants on chicken carcasses. Lastly, the fecal contaminants on chicken carcasses are extracted using the threshold segmentation and mathematical morphology. The experimental results show that the accuracy rates of the detection for the fecal contaminants of ceca, colon and duodenum are 100%, 100% and 96% respectively, and the total accuracy rate of the detection is 93.3% using 60 samples of chicken carcasses.

赵进辉, 吁芳, 吴瑞梅, 刘木华, 姚明印. 基于分段主成分分析与波段比的鸡胴体表面粪便污染物检测[J]. 激光与光电子学进展, 2011, 48(7): 073001. Zhao Jinhui, Yu Fang, Wu Ruimei, Liu Muhua, Yao Mingyin. Detection of Fecal Contaminants on Chicken Carcasses Using Segmented Principal Component Analysis and Band Ratio Algorithm[J]. Laser & Optoelectronics Progress, 2011, 48(7): 073001.

本文已被 1 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!