本文選題：高光譜圖像 + 墨粉種類鑒別��； 參考：《發(fā)光學報》2017年05期

【摘要】：為了使用快速、無損的方法區(qū)分激光打印文件使用的墨粉種類,利用高光譜成像技術結合化學計量法對6種激光打印墨粉的光譜數(shù)據進行建模和種類鑒別的研究。利用可見-近紅外高光譜成像儀采集400~1 000 nm波段內的光譜數(shù)據,采用Savitzky Golay平滑、標準化、多元散射校正和標準正態(tài)變量變換4種方法分別對光譜數(shù)據進行預處理,而后分別建立隨機森林(RF)、K最近鄰(KNN)、支持向量機(SVM)、偏最小二乘判別分析(PLS-DA)和簇類獨立軟模式(SIMCA)模型,進而實現(xiàn)激光打印墨粉的種類鑒別。利用準確率、拒識率和誤識率3個指標作為模型評價標準。實驗結果顯示,SVM和PLS-DA模型的效果最佳,準確率為100%,拒識率和誤識率為0�；诳梢�-近紅外高光譜成像技術可以實現(xiàn)激光打印墨粉的快速種類鑒別。
[Abstract]:In order to use fast and lossless methods to distinguish the toner types used in laser printing documents, the spectral data of six laser printed toner were modeled and identified by hyperspectral imaging technique combined with chemometrics. The spectral data were collected by using a visible-near infrared hyperspectral imager in the wavelength of 400,000nm. The spectral data were preprocessed by Savitzky Golay smoothing, standardization, multivariate scattering correction and standard normal variable transformation. Then, the following models were established, such as KNN, SVMN, PLS-DAA and SIMCA, respectively, and the classification of laser printing toner was realized by using the model of random forest fission K nearest neighbor, support vector machine (SVM), partial least squares discriminant analysis (PLS-DA) and cluster independent soft mode (Simca), respectively. The accuracy rate, rejection rate and error rate are used as the evaluation criteria of the model. Experimental results show that SVM and PLS-DA model have the best effect, the accuracy is 100 and the rejection rate and false recognition rate are 0. Based on visible-near-infrared hyperspectral imaging technology, laser printing toner can be quickly identified.
【作者單位】： 文件檢驗鑒定公安部重點實驗室(中國刑警學院);浙江警察學院刑事科學技術系;司法部司法鑒定科學技術研究所;
【基金】：文件檢驗鑒定公安部重點實驗室(中國刑事警察學院)課題(2015KFKT09) 浙江警察學院校局合作項目(2016XJY014)資助~~
【分類號】：TP334.8;TP391.41
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本文編號：1875256