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We propose online learning algorithms for structural SVM that has promising applications in large-scale learning. A framework is introduced for analyzing the online learning of structural SVM from primal perspective to dual perspective. The task of minimizing the primal objective function is converted to incremental increasing of the dual objective function. The model's parameter is learned through...
A computational model of visual attention was developed for regions of small defects in a large background for inspection of surface quality in the line, simulating human visual attention mechanism. Firstly global features were extracted from an input image by Law's rules. Secondly Local features were extracted and evaluated through Itti's method and inspection requirements. Then the local features...
In order to improve tracking estimation accuracy of square-root unscented Kalman particle filter (SRUKFPF), a new particle filter algorithm of update SRUKF based on iterated measurements is proposed. The algorithm produces the important density function of particle filter using maximum posteriori estimate of iterated square-root unscented Kalman filter, and amends the state covariance using Levenberg-Marquardt...
In many applications of wireless sensor networks, location is very important data. Location data can come from manual setting or GPS device. However, manual setting requires huge cost of human time, and GPS setting requires expensive device cost. Both approaches are not applicable to localization task of large scale wireless sensor networks. In this paper, we propose an accurate and efficient localization...
Sensor scheduling for target tracking in a sensor network is a research hotspot for its effectiveness in improving performance of the network. If numbers of targets and sensors are very large, the scale of the problem may be too large to solve using traditional methods. A method based on modified particle swarm optimization algorithm (MPSO) is proposed to solve the problem. Firstly, extended Kalman...
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