[Objective] The aim of this study was to extract effective feature bands of damaged rice leaves by planthoppers to make identification and classification rapidly from great amounts of imaging spectral data. [Method] T...[Objective] The aim of this study was to extract effective feature bands of damaged rice leaves by planthoppers to make identification and classification rapidly from great amounts of imaging spectral data. [Method] The experiment, using multi-spectral imaging system, acquired the multi-spectral images of damaged rice leaves from band 400 to 720 nm by interval of 5 nm. [Result] According to the principle of band index, it was calculated that the bands at 515, 510, 710, 555, 630, 535, 505, 530 and 595 nm were having high band index value with rich information and little correlation. Furthermore, the experiment used two classification methods and calcu-lated the classification accuracy higher than 90.00% for feature bands and ful bands of damaged rice leaves by planthoppers respectively. [Conclusion] It can be con-cluded that these bands can be considered as effective feature bands to identify damaged rice leaves by planthoppers quickly from a large scale of crops.展开更多
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 pape...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.展开更多
Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matri...Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matrix based projection algorithm is performed to maximize between-class differences, obtaining effective component for classification;then high-order statistics are utilized as the features to describe the mainstream line in the principal component obtained.Experiments are performed to verify the applicability of the algorithm.The results both on synthesized and real scenes indicate that this approach could extract the mainstream line of the Yellow River automatically, and has a high precision in mainstream line detection.展开更多
A multispectral imaging system consisting of liquid crystal tunable filter(LCTF)and charge coupled device(CCD)camera was used to collect the images of fingernail samples at intervals of 10 nm during the spectral range...A multispectral imaging system consisting of liquid crystal tunable filter(LCTF)and charge coupled device(CCD)camera was used to collect the images of fingernail samples at intervals of 10 nm during the spectral range of 450-1 000 nm,and a multispectral image of human fingernails containing 56 bands was obtained.The accurate reflectivity information of fingernails was obtained through referring whiteboard comparative measurement method.Principal component analysis(PCA)and band index method were used to reduce the dimension of the sample images respectively and two feature spaces were obtained.Spectral angle mapping(SAM)was used to classify human fingernails in these two feature spaces.The classification accuracy were above 92.5%and 82.9%respectively.Therefore,the feature space obtained by the PCA can be used as the characteristic spectrum of human fingernails,which provides a reliable basis for the analysis of multispectral spectrum of fingernails and human health assessment in the future.展开更多
基金Supported by National Natural Science Foundation of China under Grant(No.60968001,61168003)Natural Science Foundation of Yunnan Province under Grant(No.2011FZ079,2009CD047)National Training Programs of Innovation and Entrepreneurship for Undergraduates under Grant(No.201210681005,201310681004)~~
文摘[Objective] The aim of this study was to extract effective feature bands of damaged rice leaves by planthoppers to make identification and classification rapidly from great amounts of imaging spectral data. [Method] The experiment, using multi-spectral imaging system, acquired the multi-spectral images of damaged rice leaves from band 400 to 720 nm by interval of 5 nm. [Result] According to the principle of band index, it was calculated that the bands at 515, 510, 710, 555, 630, 535, 505, 530 and 595 nm were having high band index value with rich information and little correlation. Furthermore, the experiment used two classification methods and calcu-lated the classification accuracy higher than 90.00% for feature bands and ful bands of damaged rice leaves by planthoppers respectively. [Conclusion] It can be con-cluded that these bands can be considered as effective feature bands to identify damaged rice leaves by planthoppers quickly from a large scale of crops.
基金University-Industry-Science Partnership Project of Guangdong Province and Ministry of Education,China(No.2012B091000155)Strategic Emerging Industries Project of Guangdong Province(No.2011912027)
文摘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.
基金supported by the Flood Control Foundation of Yellow River Conservancy Commissionthe 2007 Key Supporting Project on Undergraduate Graduation Thesis of North-western Polytechnical University.
文摘Mainstream line is significant for the Yellow River situation forecasting and flood control.An effective statistical feature extraction method is proposed in this paper.In this method, a between-class scattering matrix based projection algorithm is performed to maximize between-class differences, obtaining effective component for classification;then high-order statistics are utilized as the features to describe the mainstream line in the principal component obtained.Experiments are performed to verify the applicability of the algorithm.The results both on synthesized and real scenes indicate that this approach could extract the mainstream line of the Yellow River automatically, and has a high precision in mainstream line detection.
基金Nationnal Natural Science Foundation of China(No.61605176)
文摘A multispectral imaging system consisting of liquid crystal tunable filter(LCTF)and charge coupled device(CCD)camera was used to collect the images of fingernail samples at intervals of 10 nm during the spectral range of 450-1 000 nm,and a multispectral image of human fingernails containing 56 bands was obtained.The accurate reflectivity information of fingernails was obtained through referring whiteboard comparative measurement method.Principal component analysis(PCA)and band index method were used to reduce the dimension of the sample images respectively and two feature spaces were obtained.Spectral angle mapping(SAM)was used to classify human fingernails in these two feature spaces.The classification accuracy were above 92.5%and 82.9%respectively.Therefore,the feature space obtained by the PCA can be used as the characteristic spectrum of human fingernails,which provides a reliable basis for the analysis of multispectral spectrum of fingernails and human health assessment in the future.
