This paper investigates a subclass of translations between logical systems, called the preservative translations, which preserve the satisfiability and the unsatisfiability of formulas. The definition of preservative ...
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The distribution difference among multiple data domains has been considered for the cross-domain text classification problem. In this study, we show two new observations along this line. First, the data distribution d...
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The coordination between agent service and Web service is the key factor for intelligent Web service management in the multi-agent based Web service framework. In view of the drawbacks of existing coordination approac...
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Automatic image annotation has become an important and challenging problem due to the existence of semantic gap. In this paper, we firstly extend probabilistic latent semantic analysis (PLSA) to model continuous quant...
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ISBN:
(纸本)9781424442959
Automatic image annotation has become an important and challenging problem due to the existence of semantic gap. In this paper, we firstly extend probabilistic latent semantic analysis (PLSA) to model continuous quantity. In addition, corresponding Expectation-Maximization (EM) algorithm is derived to determine the model parameters. Furthermore, in order to deal with the data of different modalities in terms of their characteristics, we present a semantic annotation model which employs continuous PLSA and standard PLSA to model visual features and textual words respectively. The model learns the correlation between these two modalities by an asymmetric learning approach and then it can predict semantic annotation for unseen images. We compare our approach with several state-of-the-art approaches on a standard Corel dataset. The experiment results show that our approach performs more effectively and accurately.
Understanding and modeling the function of the neurons and neural systems are primary goal of systems neuroscience. Sparse coding theory demonstrates that the neurons in primary visual cortex form a sparse representat...
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ISBN:
(纸本)9781424442959
Understanding and modeling the function of the neurons and neural systems are primary goal of systems neuroscience. Sparse coding theory demonstrates that the neurons in primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics. In this paper, we propose a novel sparse coding model based on structural similarity (SS_SC) for natural image feature extraction. The advantage for our model is to be able to preserve structural information from a scene, which human visual perception is highly adapted for. Using the proposed sparse coding model, the validity of image feature extraction is testified. Furthermore, compared with standard sparse coding (SC) model, the experimental results show that the quality of reconstructed images obtained by our method outperforms the SC method.
License plate detection plays an important role in vehicle license plate recognition for intelligent transport systems. This paper presents a robust method for license plate detection. As we observed, license plate ar...
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License plate detection plays an important role in vehicle license plate recognition for intelligent transport systems. This paper presents a robust method for license plate detection. As we observed, license plate area contains rich corner points' information. The whole procedure is as follows: (1) Extract the interesting points using Harris corner detector in enhanced car image. (2) Cluster the corner points and apply outlier detection to every cluster. (3) Merge the clusters close to each other and use the gradient information to find the proper block in which the license plate area is enclosed. (4) Train a simple cascade classifier to classify the blocks into two categories: The area possibly containing license plate and those not. (5) Detect the plate region by the rectangle window. Experimental results demonstrate the robustness and the generality of our method.
Conventional pulse compression use a periodical echo of single receive antenna, which is modulated by a certain carrier-frequency, in other words, single spectrum is exploited. But for MIMO radar, as the multi-carrier...
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Conventional pulse compression use a periodical echo of single receive antenna, which is modulated by a certain carrier-frequency, in other words, single spectrum is exploited. But for MIMO radar, as the multi-carrier-frequency signals are transmitted simultaneously, if the spectrum of the target echo after channel separation can be combined to form the whole band spectrum echo, the corresponding range resolution can improve several times as compared with the conventional method, and it will be more convenient for follow-up detection and tracking. Considering the difference between the frequency modulation band and the interval between the adjacent frequencies, the spectrum joint after channel separation will be overlapped or spaced. The methods of spectrum moving of each echo and the spectrum extrapolation with Root-MUSIC algorithm are proposed, by which high-resolution range profile of the target is obtained. Simulation results verify the validity of these methods.
Services computing is an interdisciplinary subject that devotes to bridging the gap between business services and IT services. It is recognized that Requirements Engineering is fundamental in implementing the service ...
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Separation logic is an extension of Hoare logic for reasoning about mutable heap structure. To represent separation logic in the first-order logic, there are several choices to determine what are constants, what are p...
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Fast stereoscopic video encoding becomes a highly desired technique because the stereoscopic video has been realizable for applications like TV broadcasting and consumer electronics. The stereoscopic video has high in...
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Fast stereoscopic video encoding becomes a highly desired technique because the stereoscopic video has been realizable for applications like TV broadcasting and consumer electronics. The stereoscopic video has high inter-view dependency subject to epipolar restriction, which can be used to reduce the encoding complexity. In this paper, we propose a fast inter-prediction mode selection algorithm for stereoscopic video encoding. Different from methods using disparity estimation, candidate modes are generated by sliding a window along the macro-block line restricted by the epipolar. Then the motion information is utilized to rectify the candidate modes. A selection failure handling algorithm is also proposed to preserve coding quality. The proposed algorithm is evaluated using independent H.264/AVC encoders for left and right views and can be extended to MVC. Experimental results show that encoding times of one view are reduced by 41.4% and 24.4% for HD and VGA videos respectively with little quality loss.
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