The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these...The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects.展开更多
Apostichopus japonicus(Holothuroidea,Echinodermata) is an ecological and economic species in East Asia.Conventional biometric monitoring method includes diving for samples and weighing above water,with highly variable...Apostichopus japonicus(Holothuroidea,Echinodermata) is an ecological and economic species in East Asia.Conventional biometric monitoring method includes diving for samples and weighing above water,with highly variable in weight measurement due to variation in the quantity of water in the respiratory tree and intestinal content of this species.Recently,video survey method has been applied widely in biometric detection on underwater benthos.However,because of the high flexibility of A.japonicus body,video survey method of monitoring is less used in sea cucumber.In this study,we designed a model to evaluate the wet weight of A.japonicus,using machine vision technology combined with a support vector machine(SVM) that can be used infield surveys on the A.japonicus population.Continuous dorsal images of free-moving A.japonicus individuals in seawater were captured,which also allows for the development of images of the core body edge as well as thorn segmentation.Parameters that include body length,body breadth,perimeter and area,were extracted from the core body edge images and used in SVM regression,to predict the weight of A.japonicus and for comparison with a power model.Results indicate that the use of SVM for predicting the weight of 33 A.japonicus individuals is accurate(R^2=0.99) and compatible with the power model(R^2=0.96).The image-based analysis and size-weight regression models in this study may be useful in body weight evaluation of A.japonicus in lab and field study.展开更多
This paper summarized the application of computer technology in fruit science, including crop modelling, expert system, decision support system (DSS), computer vision (CV), the Internet, 3 “S”technology, etc. The ex...This paper summarized the application of computer technology in fruit science, including crop modelling, expert system, decision support system (DSS), computer vision (CV), the Internet, 3 “S”technology, etc. The existing problems and prospects are also discussed in the paper.展开更多
基金Supported by the National Natural Science Foundation of China （No. 60872096） and the Fundamental Research Funds for the Central Universities （No. 2009B31914）.
文摘The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects.
基金Supported by the National Natural Science Foundation of China(NSFC)-Shandong Joint Fund for Marine Science Research Centers(No.U1406403)the National Key Technology Research and Development Program of China(No.2011BAD13B02)+1 种基金the National Marine Public Welfare Research Project(No.201205023)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA11020404)
文摘Apostichopus japonicus(Holothuroidea,Echinodermata) is an ecological and economic species in East Asia.Conventional biometric monitoring method includes diving for samples and weighing above water,with highly variable in weight measurement due to variation in the quantity of water in the respiratory tree and intestinal content of this species.Recently,video survey method has been applied widely in biometric detection on underwater benthos.However,because of the high flexibility of A.japonicus body,video survey method of monitoring is less used in sea cucumber.In this study,we designed a model to evaluate the wet weight of A.japonicus,using machine vision technology combined with a support vector machine(SVM) that can be used infield surveys on the A.japonicus population.Continuous dorsal images of free-moving A.japonicus individuals in seawater were captured,which also allows for the development of images of the core body edge as well as thorn segmentation.Parameters that include body length,body breadth,perimeter and area,were extracted from the core body edge images and used in SVM regression,to predict the weight of A.japonicus and for comparison with a power model.Results indicate that the use of SVM for predicting the weight of 33 A.japonicus individuals is accurate(R^2=0.99) and compatible with the power model(R^2=0.96).The image-based analysis and size-weight regression models in this study may be useful in body weight evaluation of A.japonicus in lab and field study.
文摘This paper summarized the application of computer technology in fruit science, including crop modelling, expert system, decision support system (DSS), computer vision (CV), the Internet, 3 “S”technology, etc. The existing problems and prospects are also discussed in the paper.
