Recently, a new representation for recognizing instances and categories of scenes called spatial Principal component analysis of Census Transform histograms (PACT) has shown its excellent performance in the scene imag...
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In Epilepsy EEG signal classification, the main time-frequency features can be extracted by using sparse representation with marching pursuit (MP) algorithm. However, the computational burden is so heavy that it is al...
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In Epilepsy EEG signal classification, the main time-frequency features can be extracted by using sparse representation with marching pursuit (MP) algorithm. However, the computational burden is so heavy that it is almost impossible to apply MP to real time signal processing. To reduce complexity of sparse representation, we propose to adopt harmony search method in searching the best atoms. Because harmony search method can find the best atoms in continuous time-frequency dictionary, the performance of epilepsy EEG signal classification is enhanced. The validity of this method is proved by experimental results.
Aiming at the effective approximation of sampling particle set relative to system state in observation uncertainty, a novel cost reference particle filter based on adaptive particle swarm optimization is proposed. In ...
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The existence of imbalanced data between one class and another class is an important issue to be considered in a classification problem. One of the well-known data balancing technique is the artificial oversampling, w...
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Object recognition from images is one of the essential problems in automatic imageprocessing. In this paper we focus specifically on nearest neighbor methods, which are widely used in many practical applications, not...
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Aiming at the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutters environment, a novel probabilistic data association algorithm based on multiple model particle fil...
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According to the characteristic and the requirement of multipath planning, a new multipath planning method is proposed based on network. This method includes two steps: the construction of network and multipath searc...
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According to the characteristic and the requirement of multipath planning, a new multipath planning method is proposed based on network. This method includes two steps: the construction of network and multipath searching. The construction of network proceeds in three phases: the skeleton extraction of the configuration space, the judgment of the cross points in the skeleton and how to link the cross points to form a network. Multipath searching makes use of the network and iterative penalty method (IPM) to plan multi-paths, and adjusts the planar paths to satisfy the requirement of maneuverability of unmanned aerial vehicle (UAV). In addition, a new height planning method is proposed to deal with the height planning of 3D route. The proposed algorithm can find multiple paths automatically according to distribution of terrain and threat areas with high efficiency. The height planning can make 3D route following the terrain. The simulation experiment illustrates the feasibility of the proposed method.
Deblurring camera-based document image is an important task in digital document processing, since it can improve both the accuracy of optical character recognition systems and the visual quality of document images. Tr...
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Recently, a new representation for recognizing instances and categories of scenes called spatial Principal component analysis of Census Transform histograms (PACT) has shown its excellent performance in the scene imag...
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ISBN:
(纸本)9781457720086
Recently, a new representation for recognizing instances and categories of scenes called spatial Principal component analysis of Census Transform histograms (PACT) has shown its excellent performance in the scene image classification task. PACT captures local structures of an image through the Census Transform (CT), meanwhile, large scale structures are captured by the strong correlation between neighboring CT values and the histogram. However, the original spatial PACT only simply concatenates all levels compact histograms together, and discards the difference between various levels. In order to improve this problem, we propose a multi-level kernel machine method, which computes a set of base kernels at each level of pyramid of PACT, and finds optimal weights for best fusing all these base kernels for scene recognition. Experiments on two popular benchmark datasets demonstrate that our proposed multi-level kernel machine method outperforms the spatial PACT on scene recognition. Besides, our method is easy to be implemented comparing with spatial PACT.
Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high...
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Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high fault prone metrical data are much scattered and multi-centers can represent the whole dataset better, we used artificial immune network (aiNet) algorithm to extract and simplify data from the modules that have been tested, then generated multi-centers for each network by Hierarchical Clustering. The proposed framework acquires information along with the testing process timely and adjusts the network generated by aiNet algorithm dynamically. Experimental results show that higher accuracy can be obtained by using the proposed framework.
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