The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we propose...The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.展开更多
In this paper, we deal with nonlinear ill-posed problems involving m-accretive mappings in Banach spaces. We consider a derivative and inverse free method for the imple- mentation of Lavrentiev regularization method. ...In this paper, we deal with nonlinear ill-posed problems involving m-accretive mappings in Banach spaces. We consider a derivative and inverse free method for the imple- mentation of Lavrentiev regularization method. Using general HSlder type source condition we obtain an optimal order error estimate. Also we consider the adaptive parameter choice strategy proposed by Pereverzev and Schock （2005） for choosing the regularization parameter.展开更多
It is well known that the problem on the stability of the solutions for Fredholm integral equation of the first kind is an ill-posed problem in C[a, b] or L2 [a, b]. In this paper, the representation of the solution f...It is well known that the problem on the stability of the solutions for Fredholm integral equation of the first kind is an ill-posed problem in C[a, b] or L2 [a, b]. In this paper, the representation of the solution for Fredholm integral equation of the first kind is given if it has a unique solution. The stability of the solution is proved in the reproducing kernel space, namely, the measurement errors of the experimental data cannot result in unbounded errors of the true solution. The computation of approximate solution is also stable with respect to ｜｜· ｜｜c or ｜｜L2· A numerical experiment shows that the method given in this paper is stable in the reproducing kernel space.展开更多
In current interactive television schemes, the viewpoints should be manipulated by the user. However, there is no efficient method, to assist a user in automatically identifying and tracking the optimum viewpoint when...In current interactive television schemes, the viewpoints should be manipulated by the user. However, there is no efficient method, to assist a user in automatically identifying and tracking the optimum viewpoint when the user observes the object of interest because many objects, most often humans, move rapidly and frequently. This paper proposes a novel framework for determining and tracking the virtual camera to best capture the front of the person of interest (PoI). First, one PoI is interactively chosen in a segmented 3D scene reconstructed by space carving method. Second, key points of the human torso of the PoI are detected by using a model-based method and the human's global motion including rotation and translation is estimated by using a close-formed method with 3 corresponding points. At the last step, the front direction of PoI is tracked temporally by using the unscented particle filter (UPF). Experimental results show that the method can properly compute the front direction of the PoI and robustly track the best viewpoints.展开更多
基金co-supported by the National Natural Science Foundation of China (Grant Nos. 61371134, 61071137)the National Basic Research Program of China (No. 2010CB327900)
文摘The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.
基金National Institute of Technology Karnataka, India, for the financial support
文摘In this paper, we deal with nonlinear ill-posed problems involving m-accretive mappings in Banach spaces. We consider a derivative and inverse free method for the imple- mentation of Lavrentiev regularization method. Using general HSlder type source condition we obtain an optimal order error estimate. Also we consider the adaptive parameter choice strategy proposed by Pereverzev and Schock （2005） for choosing the regularization parameter.
文摘It is well known that the problem on the stability of the solutions for Fredholm integral equation of the first kind is an ill-posed problem in C[a, b] or L2 [a, b]. In this paper, the representation of the solution for Fredholm integral equation of the first kind is given if it has a unique solution. The stability of the solution is proved in the reproducing kernel space, namely, the measurement errors of the experimental data cannot result in unbounded errors of the true solution. The computation of approximate solution is also stable with respect to ｜｜· ｜｜c or ｜｜L2· A numerical experiment shows that the method given in this paper is stable in the reproducing kernel space.
基金the Shanghai Municipal Education Commission Project (No. SDL10026)
文摘In current interactive television schemes, the viewpoints should be manipulated by the user. However, there is no efficient method, to assist a user in automatically identifying and tracking the optimum viewpoint when the user observes the object of interest because many objects, most often humans, move rapidly and frequently. This paper proposes a novel framework for determining and tracking the virtual camera to best capture the front of the person of interest (PoI). First, one PoI is interactively chosen in a segmented 3D scene reconstructed by space carving method. Second, key points of the human torso of the PoI are detected by using a model-based method and the human's global motion including rotation and translation is estimated by using a close-formed method with 3 corresponding points. At the last step, the front direction of PoI is tracked temporally by using the unscented particle filter (UPF). Experimental results show that the method can properly compute the front direction of the PoI and robustly track the best viewpoints.
