The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the origin...The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.展开更多
Based on an in-depth study of wavelet gray moment, we proposed a concept of a time-division scale level moment and gave the specific definition; ulteriorly, we discussed the factors which affected the fault diagnosis ...Based on an in-depth study of wavelet gray moment, we proposed a concept of a time-division scale level moment and gave the specific definition; ulteriorly, we discussed the factors which affected the fault diagnosis ability of a time-division scale level moment. The analysis results in the caculation of six typical fault signals show that the time-division scale level moment can be used to display the detailed information of a wavelet gray level image, extract the signal＇s characteristics effectively, and distinguish the vibration fault. Compared to the method of a wave gray moment vector, the method mentioned in this paper can provide higher calculation speed and higher capacity of fault identification, so it is more suitable for online fault diagnosis for rotating machinery.展开更多
Recognizing various traffic signs,especially the popular circular traffic signs,is an essential task for implementing advanced driver assistance system.To recognize circular traffic signs with high accuracy and robust...Recognizing various traffic signs,especially the popular circular traffic signs,is an essential task for implementing advanced driver assistance system.To recognize circular traffic signs with high accuracy and robustness,a novel approach which uses the so-called improved constrained binary fast radial symmetry（ICBFRS） detector and pseudo-zernike moments based support vector machine（PZM-SVM） classifier is proposed.In the detection stage,the scene image containing the traffic signs will be converted into Lab color space for color segmentation.Then the ICBFRS detector can efficiently capture the position and scale of sign candidates within the scene by detecting the centers of circles.In the classification stage,once the candidates are cropped out of the image,pseudo-zernike moments are adopted to represent the features of extracted pictogram,which are then fed into a support vector machine to classify different traffic signs.Experimental results under different lighting conditions indicate that the proposed method has robust detection effect and high classification accuracy.展开更多
The△I = 2 and △I = 4 staggering parameters of transition energies Eγfor normally deformed positive parity ground bands in232 Th and236,238 U nuclei are studied in framework of the symplectic extension of the intera...The△I = 2 and △I = 4 staggering parameters of transition energies Eγfor normally deformed positive parity ground bands in232 Th and236,238 U nuclei are studied in framework of the symplectic extension of the interacting vector boson model. The model parameters are obtained from the fitting procedure between the calculated excitation energies and the corresponding experimental ones. The staggering parameters represent the finite difference approximations to higher order derivatives dnEγ/d Inof the γ-ray transition energies in a△I = 2 and △I = 4 bands, which yielding multipoint formulae. The first order derivative(two-point formula) provides us with information about the dynamical moment of inertia. The staggering oscillation for the fourth order derivative(five-point formula) is about 0.5 Ke V and is even larger than that in superdeformed bands. The quite similarity in dynamical moments of inertia of the isotopes236,238 U up to high spin states indicate that the phenomenon of identical bands is not restricted to superdeformed bands.展开更多
Osteosarcoma is primary malignant neoplasms derived from cells of mesenchymal origin, and often has distinct phenotypes at different stages. The location of tumor and reaction zone can be identified by an expert in ma...Osteosarcoma is primary malignant neoplasms derived from cells of mesenchymal origin, and often has distinct phenotypes at different stages. The location of tumor and reaction zone can be identified by an expert in magnetic resonance imaging (MRI), with MRI being one of the choices for evaluating the extent of osteosarcoma. However, it is still a challenge to automatically extract tumor from its surrounding tissues because of their low intensity differences in MRI. We investigated an approach based on Zernike moment and support vector machine (SVM) for osteosarcoma segmentation in T1-weighted image (TIWI). Firstly, the different order moments around each pixel are calculated in small windows. Secondly, the grayscale and the module values of different order moments are used as a texture feature vector which is then used as the training set for SVM. Finally, an SVM classifier is trained based on this set of features to identify the osteosarcoma, and the segmented tumor tissue is rendered in 3D by the ray casting algorithm based on graphics processing unit (GPU). The performance of the method is validated on T1WI, showing that the segmentation method has a high similarity index with the expert's manual segmentation.展开更多
基金the National Natural Science Foundation of China (60303029)
文摘The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.
基金This paper is supported by the National Natural Science Foundation of China (NSFC) under Grant No.50775083
文摘Based on an in-depth study of wavelet gray moment, we proposed a concept of a time-division scale level moment and gave the specific definition; ulteriorly, we discussed the factors which affected the fault diagnosis ability of a time-division scale level moment. The analysis results in the caculation of six typical fault signals show that the time-division scale level moment can be used to display the detailed information of a wavelet gray level image, extract the signal＇s characteristics effectively, and distinguish the vibration fault. Compared to the method of a wave gray moment vector, the method mentioned in this paper can provide higher calculation speed and higher capacity of fault identification, so it is more suitable for online fault diagnosis for rotating machinery.
基金Supported by the Program for Changjiang Scholars and Innovative Research Team (2008)Program for New Centoury Excellent Talents in University(NCET-09-0045)+1 种基金the National Nat-ural Science Foundation of China (60773044,61004059)the Natural Science Foundation of Beijing(4101001)
文摘Recognizing various traffic signs,especially the popular circular traffic signs,is an essential task for implementing advanced driver assistance system.To recognize circular traffic signs with high accuracy and robustness,a novel approach which uses the so-called improved constrained binary fast radial symmetry（ICBFRS） detector and pseudo-zernike moments based support vector machine（PZM-SVM） classifier is proposed.In the detection stage,the scene image containing the traffic signs will be converted into Lab color space for color segmentation.Then the ICBFRS detector can efficiently capture the position and scale of sign candidates within the scene by detecting the centers of circles.In the classification stage,once the candidates are cropped out of the image,pseudo-zernike moments are adopted to represent the features of extracted pictogram,which are then fed into a support vector machine to classify different traffic signs.Experimental results under different lighting conditions indicate that the proposed method has robust detection effect and high classification accuracy.
文摘The△I = 2 and △I = 4 staggering parameters of transition energies Eγfor normally deformed positive parity ground bands in232 Th and236,238 U nuclei are studied in framework of the symplectic extension of the interacting vector boson model. The model parameters are obtained from the fitting procedure between the calculated excitation energies and the corresponding experimental ones. The staggering parameters represent the finite difference approximations to higher order derivatives dnEγ/d Inof the γ-ray transition energies in a△I = 2 and △I = 4 bands, which yielding multipoint formulae. The first order derivative(two-point formula) provides us with information about the dynamical moment of inertia. The staggering oscillation for the fourth order derivative(five-point formula) is about 0.5 Ke V and is even larger than that in superdeformed bands. The quite similarity in dynamical moments of inertia of the isotopes236,238 U up to high spin states indicate that the phenomenon of identical bands is not restricted to superdeformed bands.
文摘Osteosarcoma is primary malignant neoplasms derived from cells of mesenchymal origin, and often has distinct phenotypes at different stages. The location of tumor and reaction zone can be identified by an expert in magnetic resonance imaging (MRI), with MRI being one of the choices for evaluating the extent of osteosarcoma. However, it is still a challenge to automatically extract tumor from its surrounding tissues because of their low intensity differences in MRI. We investigated an approach based on Zernike moment and support vector machine (SVM) for osteosarcoma segmentation in T1-weighted image (TIWI). Firstly, the different order moments around each pixel are calculated in small windows. Secondly, the grayscale and the module values of different order moments are used as a texture feature vector which is then used as the training set for SVM. Finally, an SVM classifier is trained based on this set of features to identify the osteosarcoma, and the segmented tumor tissue is rendered in 3D by the ray casting algorithm based on graphics processing unit (GPU). The performance of the method is validated on T1WI, showing that the segmentation method has a high similarity index with the expert's manual segmentation.
