测试科学与仪器
測試科學與儀器
측시과학여의기
JOURNAL OF MEASUREMENT SCIENCE AND INSTRUMENTATION
2014年
4期
46-51
,共6页
何少岩%陈舜儿%翟浩田%刘伟平
何少巖%陳舜兒%翟浩田%劉偉平
하소암%진순인%적호전%류위평
宣纸%多光谱成像%特征分析%数学形态学
宣紙%多光譜成像%特徵分析%數學形態學
선지%다광보성상%특정분석%수학형태학
rice paper%multispectral imaging%texture analysis%mathematical morphology
国画艺术品的科技取证与鉴别受到各领域的关注。宣纸作为国画的重要载体,其特征提取和分析方法有较高的研究意义。本文使用多光谱成像技术对宣纸进行形态学特征分析。实验采用多光谱成像系统获取宣纸在不同波长通道下的光谱图片,继而采用纹理参数统计对光谱图像进行测量,获得宣纸特征的敏感波段;采用数学形态学和灰度统计原理建立宣纸形态学特征分析模型,得到一维特征向量。为了评价特征向量的准确度,实验将特征向量输入到支持向量机(SVM)分类器进行检测分类。结果表明,宣纸的差异化特征在550 nm 波段下最为明显;由模型输出的特征向量分类正确率为96%。本文提出的宣纸分析模型能够将大部分种类的宣纸特征准确提取,并具有一定的高效性。
國畫藝術品的科技取證與鑒彆受到各領域的關註。宣紙作為國畫的重要載體,其特徵提取和分析方法有較高的研究意義。本文使用多光譜成像技術對宣紙進行形態學特徵分析。實驗採用多光譜成像繫統穫取宣紙在不同波長通道下的光譜圖片,繼而採用紋理參數統計對光譜圖像進行測量,穫得宣紙特徵的敏感波段;採用數學形態學和灰度統計原理建立宣紙形態學特徵分析模型,得到一維特徵嚮量。為瞭評價特徵嚮量的準確度,實驗將特徵嚮量輸入到支持嚮量機(SVM)分類器進行檢測分類。結果錶明,宣紙的差異化特徵在550 nm 波段下最為明顯;由模型輸齣的特徵嚮量分類正確率為96%。本文提齣的宣紙分析模型能夠將大部分種類的宣紙特徵準確提取,併具有一定的高效性。
국화예술품적과기취증여감별수도각영역적관주。선지작위국화적중요재체,기특정제취화분석방법유교고적연구의의。본문사용다광보성상기술대선지진행형태학특정분석。실험채용다광보성상계통획취선지재불동파장통도하적광보도편,계이채용문리삼수통계대광보도상진행측량,획득선지특정적민감파단;채용수학형태학화회도통계원리건립선지형태학특정분석모형,득도일유특정향량。위료평개특정향량적준학도,실험장특정향량수입도지지향량궤(SVM)분류기진행검측분류。결과표명,선지적차이화특정재550 nm 파단하최위명현;유모형수출적특정향량분류정학솔위96%。본문제출적선지분석모형능구장대부분충류적선지특정준학제취,병구유일정적고효성。
Computer forensics and identification for traditional Chinese painting arts have caught the attention of various fields .Rice paper’s feature extraction and analysis methods are of high significance for the rice paper is an important carrier of traditional Chinese painting arts .In this paper ,rice paper’s morphological feature analysis is done using multi spectral imaging technology .The multispectral imaging system is utilized to acquire rice paper ’s spectral images in different wave-length channels ,and then those spectral images are measured using texture parameter statistics to acquire sensitive bands for rice paper’s feature .The mathematical morphology and grayscale statistical principle are utilized to establish a rice paper ’s morphological feature analytical model which is used to acquire rice paper’s one-dimensional vector .For the purpose of eval-uating these feature vectors’ accuracy ,they are entered into the support vector machine (SVM) classifier for detection and classification .The results show that the rice paper’s feature is out loud in the spectral band 550 nm ,and the average classifi-cation accuracy of feature vectors output from the analytical model is 96% .The results indicate that the rice paper’s feature analytical model can extract most of rice paper’s features with accuracy and efficiency .