We present a novel method to segment an object from multiview images using level set method. Our approach takes advantage of the unique property of level set method in the flexibility of objective energy function design and the adaptability to cut boundary with arbitrary topology. We introduce an iterating optimized 3D level set framework for view coherent segmentation and propose three forces in this framework to drive the convergence of level set to the ideal boundary. In between, the point cloud term and the edge term are designed to give an as-good-as-possible boundary indicator for the level set function, while the local color discriminative classifier is iteratively updated with the multiview silhouette and the 3D point cloud to drive the deformation of the zero level set. Extensive experimental results demonstrate that our approach can produce much more accurate edge localization and more coherent segmentation result across views, compared with the state-of-the-art methods, even for the case of very challenging foreground topologies and ambiguous foreground-background color distribution.
Strap-down inertial navigation system (SINS) is widely used in military field, to facilitate the study of SINS algorithms and various coupled navigation algorithms, a simulation system of SINS is designed. Based on modular design, with good portability and expansibility, the system consists of four independent modules: analysis module of motion state, trajectory simulator, IMU simulation module and SINS calculation module. With graphical interface, the system can control every motion state of the trajectory, which is convenient to generate various trajectories efficiently. Using rotation vector attitude algorithm to process simulation data, experiment results show that the attitude, velocity and position error is consistent with the theoretical value, which verifies the rationality of the simulation model and the availability of the simulation system.
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