This paper presents a new approach to the extraction of a moving object from video sequence. The method is based on morphological motion filter using connected operator and a proposed new filtering criterion. The morp...This paper presents a new approach to the extraction of a moving object from video sequence. The method is based on morphological motion filter using connected operator and a proposed new filtering criterion. The morphological motion filter aims to detect motion which is distinct from that of the background, and thereby locates independently moving physical objects in the scenes. Experiments show that the algorithm can extract object from moving backgrounds efficiently.展开更多
Determination of relative three-dimensional (3D) position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fiel...Determination of relative three-dimensional (3D) position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fields such as photogrammetry. A solution to pose and motion estimation problem that uses two-dimensional (2D) intensity images from a single camera is de- sirable for real-time applications. The difficulty in performing this measurement is that the process of projecting 3D object features to 2D images is a nonlinear transformation. In this paper, the 3D transformation is modeled as a nonlinear stochastic system with the state estimation providing six degrees-of-freedom motion and position values, using line features in image plane as measuring inputs and dual quaternion to represent both rotation and translation in a unified notation. A filtering method called the Gaussian particle filter (GPF) based on the particle filtering concept is presented for 3D pose and motion estimation of a moving target from monocular image sequences. The method has been implemented with simulated data, and simulation results are provided along with comparisons to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) to show the relative advantages of the GPF. Simulation results showed that GPF is a superior alternative to EKF and UKF.展开更多
This paper develops a fast filtering algorithm based on vibration systems theory and neural information exchange approach. The characters, including the derivation process and parameter analysis, are discussed and the...This paper develops a fast filtering algorithm based on vibration systems theory and neural information exchange approach. The characters, including the derivation process and parameter analysis, are discussed and the feasibility and the effectiveness are testified by the filtering performance compared with various filtering methods, such as the fast wavelet transform algorithm, the particle filtering method and our previously developed single degree of freedom vibration system filtering algorithm, according to simulation and practical approaches. Meanwhile, the comparisons indicate that a significant advantage of the proposed fast filtering algorithm is its extremely fast filtering speed with good filtering perfi^rmance. Further, the developed fast filtering algorithm is applied to the navigation and positioning system of the micro motion robot, which is a high real-time requirement for the signals preprocessing. Then, the preprocessing data is used to estimate the heading angle error and the attitude angle error of the micro motion robot. The estimation experiments illustrate the high practicality of the proposed fast filtering algorithm.展开更多
Recognition of the human actions by computer vision has become an active research area in recent years. Due to the speed and the high similarity of the actions, the current algorithms cannot get high recognition rate....Recognition of the human actions by computer vision has become an active research area in recent years. Due to the speed and the high similarity of the actions, the current algorithms cannot get high recognition rate. A new recognition method of the human action is proposed with the multi-scale directed depth motion maps(MsdDMMs) and Log-Gabor filters. According to the difference between the speed and time order of an action, MsdDMMs is proposed under the energy framework. Meanwhile, Log-Gabor is utilized to describe the texture details of MsdDMMs for the motion characteristics. It can easily satisfy both the texture characterization and the visual features of human eye. Furthermore, the collaborative representation is employed as action recognition by the classification. Experimental results show that the proposed algorithm, which is applied in the MSRAction3 D dataset and MSRGesture3 D dataset, can achieve the accuracy of 95.79% and 96.43% respectively. It also has higher accuracy than the existing algorithms, such as super normal vector(SNV), hierarchical recurrent neural network(Hierarchical RNN).展开更多
Representing earthquake ground： motion as time varying ARMA model, the instantaneous spectrum can only be determined by the time varying coefficients of the corresponding ARMA model. In this paper, unscented Kalman f...Representing earthquake ground： motion as time varying ARMA model, the instantaneous spectrum can only be determined by the time varying coefficients of the corresponding ARMA model. In this paper, unscented Kalman filter is applied to estimate the time varying coefficients. The comparison between the estimation results of unscented Kalman filter and Kalman filter methods shows that unscented Kalman filter can more precisely represent the distribution of the spectral peaks in time-frequency plane than Kalman filter, and its time and frequency resolution is finer which ensures its better ability to track the local properties of earthquake ground motions and to identify the systems with nonlinearity or abruptness. Moreover, the estimation results of ARMA models with different orders indicate that the theoretical frequency resolving power of ARMA model which was usually ignored in former studies has great effect on the estimation precision of instantaneous spectrum and it should be taken as one of the key factors in order selection of ARMA model.展开更多
文摘This paper presents a new approach to the extraction of a moving object from video sequence. The method is based on morphological motion filter using connected operator and a proposed new filtering criterion. The morphological motion filter aims to detect motion which is distinct from that of the background, and thereby locates independently moving physical objects in the scenes. Experiments show that the algorithm can extract object from moving backgrounds efficiently.
基金Supported by the National Natural Science Foundation of China （ No. 60872123 ） and the Guangdong Provincial Natural Science Foundation （No. U0835001 ）.
基金Project (No. 2006J0017) supported by the Natural Science Foundation of Fujian Province, China
文摘Determination of relative three-dimensional (3D) position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fields such as photogrammetry. A solution to pose and motion estimation problem that uses two-dimensional (2D) intensity images from a single camera is de- sirable for real-time applications. The difficulty in performing this measurement is that the process of projecting 3D object features to 2D images is a nonlinear transformation. In this paper, the 3D transformation is modeled as a nonlinear stochastic system with the state estimation providing six degrees-of-freedom motion and position values, using line features in image plane as measuring inputs and dual quaternion to represent both rotation and translation in a unified notation. A filtering method called the Gaussian particle filter (GPF) based on the particle filtering concept is presented for 3D pose and motion estimation of a moving target from monocular image sequences. The method has been implemented with simulated data, and simulation results are provided along with comparisons to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) to show the relative advantages of the GPF. Simulation results showed that GPF is a superior alternative to EKF and UKF.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.60901074,51075092,61005076,and 61175107)the National High Technology Research and Development Program of China(Grant No.2007AA042105)the Natural Science Foundation of Heilongjiang Province,China(Grant No.E200903)
文摘This paper develops a fast filtering algorithm based on vibration systems theory and neural information exchange approach. The characters, including the derivation process and parameter analysis, are discussed and the feasibility and the effectiveness are testified by the filtering performance compared with various filtering methods, such as the fast wavelet transform algorithm, the particle filtering method and our previously developed single degree of freedom vibration system filtering algorithm, according to simulation and practical approaches. Meanwhile, the comparisons indicate that a significant advantage of the proposed fast filtering algorithm is its extremely fast filtering speed with good filtering perfi^rmance. Further, the developed fast filtering algorithm is applied to the navigation and positioning system of the micro motion robot, which is a high real-time requirement for the signals preprocessing. Then, the preprocessing data is used to estimate the heading angle error and the attitude angle error of the micro motion robot. The estimation experiments illustrate the high practicality of the proposed fast filtering algorithm.
基金Sponsored by the Jiangsu Prospective Joint Research Project(Grant No.BY2016022-28)
文摘Recognition of the human actions by computer vision has become an active research area in recent years. Due to the speed and the high similarity of the actions, the current algorithms cannot get high recognition rate. A new recognition method of the human action is proposed with the multi-scale directed depth motion maps(MsdDMMs) and Log-Gabor filters. According to the difference between the speed and time order of an action, MsdDMMs is proposed under the energy framework. Meanwhile, Log-Gabor is utilized to describe the texture details of MsdDMMs for the motion characteristics. It can easily satisfy both the texture characterization and the visual features of human eye. Furthermore, the collaborative representation is employed as action recognition by the classification. Experimental results show that the proposed algorithm, which is applied in the MSRAction3 D dataset and MSRGesture3 D dataset, can achieve the accuracy of 95.79% and 96.43% respectively. It also has higher accuracy than the existing algorithms, such as super normal vector(SNV), hierarchical recurrent neural network(Hierarchical RNN).
基金Project supported by the National Natural Science Foundation of China (No.50008017)
文摘Representing earthquake ground： motion as time varying ARMA model, the instantaneous spectrum can only be determined by the time varying coefficients of the corresponding ARMA model. In this paper, unscented Kalman filter is applied to estimate the time varying coefficients. The comparison between the estimation results of unscented Kalman filter and Kalman filter methods shows that unscented Kalman filter can more precisely represent the distribution of the spectral peaks in time-frequency plane than Kalman filter, and its time and frequency resolution is finer which ensures its better ability to track the local properties of earthquake ground motions and to identify the systems with nonlinearity or abruptness. Moreover, the estimation results of ARMA models with different orders indicate that the theoretical frequency resolving power of ARMA model which was usually ignored in former studies has great effect on the estimation precision of instantaneous spectrum and it should be taken as one of the key factors in order selection of ARMA model.
