The problem of determining the pose of an object in 3-D space is essential in many computer vision applications. In this paper, a model-based approach for solving this problem is proposed. This approach does not requi...The problem of determining the pose of an object in 3-D space is essential in many computer vision applications. In this paper, a model-based approach for solving this problem is proposed. This approach does not require the knowledge of point-to-point correspondences between 3-D points on the model and 2-D points in the observed image. The spatial location of the object is iteratively estimated and updated from the values globally defined over the model image and the observed image.展开更多
The purpose of initial orbit determination,especially in the case of angles-only data for observation,is to obtain an initial estimate that is close enough to the true orbit to enable subsequent precision orbit determ...The purpose of initial orbit determination,especially in the case of angles-only data for observation,is to obtain an initial estimate that is close enough to the true orbit to enable subsequent precision orbit determination processing to be successful.However,the classical angles-only initial orbit determination methods cannot deal with the observation data whose Earth-central angle is larger than 360°.In this paper,an improved double r-iteration initial orbit determination method to deal with the above case is presented to monitor geosynchronous Earth orbit objects for a spacebased surveillance system.Simulation results indicate that the improved double r-iteration method is feasible,and the accuracy of the obtained initial orbit meets the requirements of re-acquiring the object.展开更多
The enhancement of radio frequency identification(RFID) technology to track and trace objects has attracted a lot of attention from the healthcare and the supply chain industry.However,RFID systems do not always funct...The enhancement of radio frequency identification(RFID) technology to track and trace objects has attracted a lot of attention from the healthcare and the supply chain industry.However,RFID systems do not always function reliably under complex and variable deployment environment.In many cases,RFID systems provide only probabilistic observations of object states.Thus,an approach to predict,record and track real world object states based upon probabilistic RFID observations is required.Hidden Markov model(HMM) has been used in the field of probabilistic location determination.But the inherent duration probability density of a state in HMM is exponential,which may be inappropriate for modeling of object location transitions.Hence,in this paper,we put forward a hidden semi-Markov model(HSMM) based approach for probabilistic location determination. We evaluated its performance comparing with that of the HMM-based approach.The results show that the HSMM-based approach provides a more accurate determination of real world object states based on observation data.展开更多
文摘The problem of determining the pose of an object in 3-D space is essential in many computer vision applications. In this paper, a model-based approach for solving this problem is proposed. This approach does not require the knowledge of point-to-point correspondences between 3-D points on the model and 2-D points in the observed image. The spatial location of the object is iteratively estimated and updated from the values globally defined over the model image and the observed image.
文摘The purpose of initial orbit determination,especially in the case of angles-only data for observation,is to obtain an initial estimate that is close enough to the true orbit to enable subsequent precision orbit determination processing to be successful.However,the classical angles-only initial orbit determination methods cannot deal with the observation data whose Earth-central angle is larger than 360°.In this paper,an improved double r-iteration initial orbit determination method to deal with the above case is presented to monitor geosynchronous Earth orbit objects for a spacebased surveillance system.Simulation results indicate that the improved double r-iteration method is feasible,and the accuracy of the obtained initial orbit meets the requirements of re-acquiring the object.
基金the National High Technology Research and Development Program(863) of China(No. 2006AA04A114)
文摘The enhancement of radio frequency identification(RFID) technology to track and trace objects has attracted a lot of attention from the healthcare and the supply chain industry.However,RFID systems do not always function reliably under complex and variable deployment environment.In many cases,RFID systems provide only probabilistic observations of object states.Thus,an approach to predict,record and track real world object states based upon probabilistic RFID observations is required.Hidden Markov model(HMM) has been used in the field of probabilistic location determination.But the inherent duration probability density of a state in HMM is exponential,which may be inappropriate for modeling of object location transitions.Hence,in this paper,we put forward a hidden semi-Markov model(HSMM) based approach for probabilistic location determination. We evaluated its performance comparing with that of the HMM-based approach.The results show that the HSMM-based approach provides a more accurate determination of real world object states based on observation data.
