Classifying the texture of granules in 2D images has aroused manifold research atten-tion for its technical challenges in image processing areas.This letter presents an aggregate texture identification approach by joi...Classifying the texture of granules in 2D images has aroused manifold research atten-tion for its technical challenges in image processing areas.This letter presents an aggregate texture identification approach by jointly using Gray Level Co-occurrence Probability(GLCP) and BP neural network techniques.First, up to 8 GLCP-associated texture feature parameters are defined and computed, and these consequent parameters next serve as the inputs feeding to the BP neural network to calculate the similarity to any of given aggregate texture type.A finite number of aggregate images of 3 kinds, with each containing specific type of mineral particles, are put to the identification test, experimentally proving the feasibility and robustness of the proposed method.展开更多
Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural...Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.展开更多
The use of computer vision for estimating quality in agriculture products has become wide spread in recent years and the composition,variety,or ripeness can be estimated.On the other hand,the appearance is one of the ...The use of computer vision for estimating quality in agriculture products has become wide spread in recent years and the composition,variety,or ripeness can be estimated.On the other hand,the appearance is one of the most worrying issues for producers due to its influence on quality.In this research,computer vision technology combined with BP artificial neural network(ANN)was developed to identify soybean frogeye,mildewed soybean,worm-eaten soybean and damaged soybean.Thirty-nine characteristic parameters from color,texture and shape characteristics were computed after preprocessing the acquired soybean images.The dimensionality of the characteristic parameters was reduced from 39 dimensionalities to 12 dimensionalities using the method of principal component analysis(PCA).MALAB software was used to build a prediction model according to 12 characteristic parameters.The identification accuracies of soybean frogeye,mildewed soybean,damaged soybean and worm-eaten soybean are 96%,95%,92%,and 92%,respectively.And the accuracy for heterogeneous soybean seeds with several diseases is 90%.The results show that the prediction model constructed by BP neural network can identify the diseases of soybean seeds.And it is useful to estimate appearance quality of soybean by computer vision applying BP neural network.展开更多
An optimized damage identification method of beam combined wavelet with neural network is presented in an attempt to improve the calculation iterative speed and accuracy damage identification. The mathematical model i...An optimized damage identification method of beam combined wavelet with neural network is presented in an attempt to improve the calculation iterative speed and accuracy damage identification. The mathematical model is developed to identify the structure damage based on the theory of finite elements and rotation modal parameters. The model is integrated with BP neural network optimization approach which utilizes the Genetic algorithm optimization method. The structural rotation modal parameters are performed with the continuous wavelet transform through the Mexico hat wavelet. The location of structure damage is identified by the maximum of wavelet coefficients. Then, the multi-scale wavelet coefficients modulus maxima are used as the inputs of the BP neural network, and through training and updating the optimal weight and threshold value to obtain the ideal output which is used to describe the degree of structural damage. The obtained results demonstrate the effectiveness of the proposed approach in simultaneously improving the structural damage identification precision including the damage locating and severity.展开更多
基金Funded by Ningbo Natural Science Foundation (No.2006A610016)
文摘Classifying the texture of granules in 2D images has aroused manifold research atten-tion for its technical challenges in image processing areas.This letter presents an aggregate texture identification approach by jointly using Gray Level Co-occurrence Probability(GLCP) and BP neural network techniques.First, up to 8 GLCP-associated texture feature parameters are defined and computed, and these consequent parameters next serve as the inputs feeding to the BP neural network to calculate the similarity to any of given aggregate texture type.A finite number of aggregate images of 3 kinds, with each containing specific type of mineral particles, are put to the identification test, experimentally proving the feasibility and robustness of the proposed method.
基金Project(61201028)supported by the National Natural Science Foundation of ChinaProject(YWF-12-JFGF-060)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2011ZD51048)supported by Aviation Science Foundation of China
文摘Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.
基金We acknowledge the financial support of Heilongjiang Provincial Natural Science Foundation(ZD201303)and Youth Scientific Research Fund of Northeast Agricultural University.
文摘The use of computer vision for estimating quality in agriculture products has become wide spread in recent years and the composition,variety,or ripeness can be estimated.On the other hand,the appearance is one of the most worrying issues for producers due to its influence on quality.In this research,computer vision technology combined with BP artificial neural network(ANN)was developed to identify soybean frogeye,mildewed soybean,worm-eaten soybean and damaged soybean.Thirty-nine characteristic parameters from color,texture and shape characteristics were computed after preprocessing the acquired soybean images.The dimensionality of the characteristic parameters was reduced from 39 dimensionalities to 12 dimensionalities using the method of principal component analysis(PCA).MALAB software was used to build a prediction model according to 12 characteristic parameters.The identification accuracies of soybean frogeye,mildewed soybean,damaged soybean and worm-eaten soybean are 96%,95%,92%,and 92%,respectively.And the accuracy for heterogeneous soybean seeds with several diseases is 90%.The results show that the prediction model constructed by BP neural network can identify the diseases of soybean seeds.And it is useful to estimate appearance quality of soybean by computer vision applying BP neural network.
文摘An optimized damage identification method of beam combined wavelet with neural network is presented in an attempt to improve the calculation iterative speed and accuracy damage identification. The mathematical model is developed to identify the structure damage based on the theory of finite elements and rotation modal parameters. The model is integrated with BP neural network optimization approach which utilizes the Genetic algorithm optimization method. The structural rotation modal parameters are performed with the continuous wavelet transform through the Mexico hat wavelet. The location of structure damage is identified by the maximum of wavelet coefficients. Then, the multi-scale wavelet coefficients modulus maxima are used as the inputs of the BP neural network, and through training and updating the optimal weight and threshold value to obtain the ideal output which is used to describe the degree of structural damage. The obtained results demonstrate the effectiveness of the proposed approach in simultaneously improving the structural damage identification precision including the damage locating and severity.
