CAJ | 학술논문

利用石油及其产品具有的紫外荧光特性,搭建了一套紫外诱导多光谱成像系统. 该系统主要由3个紫外诱导光源﹑8个滤波片和1个彩色CCD相机组成. 采集了6种油品的多光谱图像,以有效光斑的24个颜色分量均值作为特征,提出了一种联合熵最大化的独立分量分析特征优化方法. K均值聚类和支持向量机识别结果表明,较改进前的ICA方法,该方法的特征优化性能得到了有效提高,油种识别率达到了92. 3%.
이용석유급기산품구유적자외형광특성,탑건료일투자외유도다광보성상계통. 해계통주요유3개자외유도광원﹑8개려파편화1개채색CCD상궤조성. 채집료6충유품적다광보도상,이유효광반적24개안색분량균치작위특정,제출료일충연합적최대화적독립분량분석특정우화방법. K균치취류화지지향량궤식별결과표명,교개진전적ICA방법,해방법적특정우화성능득도료유효제고,유충식별솔체도료92. 3%.
Based on the UV fluorescence phenomena of oil and its products, a multispectral imaging system was constructed. This system was composed of 3 UV excitation light sources, 8 optics filters and a CCD camera. Using this system, multi-spectral images of 6 kinds of oil were collected. The mean of 24 color features of effective light spots was used as the feature set. Then, a novel method called maximize the joint entropy of independent component analysis ( ICA ) was proposed for K-mean cluster and SVM recognition. It is proved that this method is better than traditional ICA for feature optimized, and the identification rate is 92. 3%. This result has positive significance for oil detection.