CAJ | 학술논문

提出一种基于高光谱成像技术的鸡胴体表面低可见污染物的双波段检测算法。首先，在所采集的高光谱数据中，选择ROI（region of interesting ）内光谱的同一性最好、同时与边缘区域平均光谱差值最大的675 nm波段图像进行二值化处理，利用区域生长法提取最大连通区域作为掩膜。再将掩膜与污染物可分辨度最大的400 nm谱段图像进行“与”操作，提取出最大面积的鸡胴体待检ROI ，最后利用标记法识别出ROI内有污染物存在的鸡胴体。试验结果表明，采用这种双波段算法，不仅可以获得能够根据鸡胴体形状及位置自适应调节的最大ROI（比已有研究方法提取的ROI面积大176％以上），而且对鸡胴体表面低可见度血液、胆汁和粪便的正确检出率平均可达81.6％。
제출일충기우고광보성상기술적계동체표면저가견오염물적쌍파단검측산법。수선，재소채집적고광보수거중，선택ROI（region of interesting ）내광보적동일성최호、동시여변연구역평균광보차치최대적675 nm파단도상진행이치화처리，이용구역생장법제취최대련통구역작위엄막。재장엄막여오염물가분변도최대적400 nm보단도상진행“여”조작，제취출최대면적적계동체대검ROI ，최후이용표기법식별출ROI내유오염물존재적계동체。시험결과표명，채용저충쌍파단산법，불부가이획득능구근거계동체형상급위치자괄응조절적최대ROI（비이유연구방법제취적ROI면적대176％이상），이차대계동체표면저가견도혈액、담즙화분편적정학검출솔평균가체81.6％。
A novel dual-band algorithm for detecting contaminants with low visibility on chicken carcass surface based on hyper-spectral image was proposed .Firstly ,The 675 nm band image ,in which the identity of the intensity within ROI (Region of In-terest)is the best and the spectrum difference between ROI and the edge of the ROI is the biggest ,was chosen from the hyper-spectral 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 meth-od 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 contamina-tions such as blood ,bile and feces was 81.6% .