Texture analysis is a fundamental field in computer vision. However,it is also a particularly difficult problem for no universal mathematical model of real world textures. By extending a new application of the fractio...Texture analysis is a fundamental field in computer vision. However,it is also a particularly difficult problem for no universal mathematical model of real world textures. By extending a new application of the fractional Fourier transform( Fr FT) in the field of texture analysis,this paper proposes an Fr FT-based method for describing textures. Firstly,based on the Radon-Wigner transform,1-D directional Fr FT filters are designed to two types of texture features,i. e.,the coarseness and directionality. Then,the frequencies with maximum and median amplitudes of the Fr FT of the input signal are regarded as the output of the 1-D directional Fr FT filter. Finally,the mean and the standard deviation are used to compose of the feature vector. Compared to the WD-based method,three benefits can be achieved with the proposed Fr FT-based method,i. e.,less memory size,lower computational load,and less disturbed by the cross-terms. The proposed method has been tested on16 standard texture images. The experimental results show that the proposed method is superior to the popular Gabor filtering-based method.展开更多
To the Editor:Lung cancer,one of the most common types of tumor,is also the leading cause of cancer death,accounting for an estimated 1.8 million deaths worldwide.Early recognition of lung cancer is critical.It has be...To the Editor:Lung cancer,one of the most common types of tumor,is also the leading cause of cancer death,accounting for an estimated 1.8 million deaths worldwide.Early recognition of lung cancer is critical.It has been established that nitric oxide and its byproducts play a role in pathophysiological processes of lung cancer,such as tumor immunity,inflammation,and lung tumor progression.[1]Exhaled nitric oxide(eNO)concentrations can be measured by non-invasive devices.The alveolar concentration of nitric oxide(CaNO)has been proposed as a marker of distal airway inflammation,but its value in nonsmall cell lung cancer(NSCLC)is unknown.展开更多
Cerebellar model articulation controller(CMAC)is a popular associative memory neural network that imitates human’s cerebellum,which allows it to learn fast and carry out local generalization efficiently.This research...Cerebellar model articulation controller(CMAC)is a popular associative memory neural network that imitates human’s cerebellum,which allows it to learn fast and carry out local generalization efficiently.This research aims to integrate evolutionary computation into fuzzy CMAC Bayesian Ying-Yang(FCMACBYY)learning,which is referred to as FCMAC-EBYY,to achieve a synergetic development in the search for optimal fuzzy sets and connection weights.Traditional evolutionary approaches are limited to small populations of short binary string length and as such are not suitable for neural network training,which involves a large searching space due to complex connections as well as real values.The methodology employed by FCMACEBYY is coevolution,in which a complex solution is decomposed into some pieces to be optimized in different populations/species and then assembled.The developed FCMAC-EBYY is compared with various neuro-fuzzy systems using a real application of traffic flow prediction.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.61003128)
文摘Texture analysis is a fundamental field in computer vision. However,it is also a particularly difficult problem for no universal mathematical model of real world textures. By extending a new application of the fractional Fourier transform( Fr FT) in the field of texture analysis,this paper proposes an Fr FT-based method for describing textures. Firstly,based on the Radon-Wigner transform,1-D directional Fr FT filters are designed to two types of texture features,i. e.,the coarseness and directionality. Then,the frequencies with maximum and median amplitudes of the Fr FT of the input signal are regarded as the output of the 1-D directional Fr FT filter. Finally,the mean and the standard deviation are used to compose of the feature vector. Compared to the WD-based method,three benefits can be achieved with the proposed Fr FT-based method,i. e.,less memory size,lower computational load,and less disturbed by the cross-terms. The proposed method has been tested on16 standard texture images. The experimental results show that the proposed method is superior to the popular Gabor filtering-based method.
基金National Natural Science Foundation of China(Nos.82170032 and 81970032)
文摘To the Editor:Lung cancer,one of the most common types of tumor,is also the leading cause of cancer death,accounting for an estimated 1.8 million deaths worldwide.Early recognition of lung cancer is critical.It has been established that nitric oxide and its byproducts play a role in pathophysiological processes of lung cancer,such as tumor immunity,inflammation,and lung tumor progression.[1]Exhaled nitric oxide(eNO)concentrations can be measured by non-invasive devices.The alveolar concentration of nitric oxide(CaNO)has been proposed as a marker of distal airway inflammation,but its value in nonsmall cell lung cancer(NSCLC)is unknown.
基金This research was supported by the Ministry of Knowledge Economy(MKE),Korea,under the Information Technology Research Center(ITRC)supervised by the National IT Industry Promotion Agency(NIPA)(NIPA-2010-(C1090-1021-0002))It was sponsored by Daegu Gyungpook Development Institute 2010.
文摘Cerebellar model articulation controller(CMAC)is a popular associative memory neural network that imitates human’s cerebellum,which allows it to learn fast and carry out local generalization efficiently.This research aims to integrate evolutionary computation into fuzzy CMAC Bayesian Ying-Yang(FCMACBYY)learning,which is referred to as FCMAC-EBYY,to achieve a synergetic development in the search for optimal fuzzy sets and connection weights.Traditional evolutionary approaches are limited to small populations of short binary string length and as such are not suitable for neural network training,which involves a large searching space due to complex connections as well as real values.The methodology employed by FCMACEBYY is coevolution,in which a complex solution is decomposed into some pieces to be optimized in different populations/species and then assembled.The developed FCMAC-EBYY is compared with various neuro-fuzzy systems using a real application of traffic flow prediction.
