Recently, researchers have discovered unexpected bumps in the detection rate curve of yet another steganographic scheme (YASS). We refer to this abnormal phenomenon as non-monotonic security performance. This paper fi...
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This paper is aimed at extraction of ontology concept from four diagnostics information. Due to the diversity and complexity of the four diagnostics information, there are still some difficulties when practicing ontol...
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Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral *** for medium-resolution remote sensing images used in urban land...
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Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral *** for medium-resolution remote sensing images used in urban landcover change monitoring,land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial ***,traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably,degrading the overall accuracy of change *** order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level,a novel change detection scheme is designed based on the spectral mixture analysis and decision-level *** spectral mixture model is selected for spectral unmixing,and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition *** proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban *** effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods(*** vector analysis and principal component analysis-based method).In particular,the proposed sub-pixel change detection approach not only provides the binary change information,but also obtains the characterization about change direction and intensity,which greatly extends the semantic meaning of the detected change targets.
Discriminant neighborhood embedding (DNE) algorithm is one of supervised linear dimensionality reduction methods. Its nonlinear version kernel discriminant neighborhood embedding (KDNE) is expected to behave well on c...
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Discriminant neighborhood embedding (DNE) algorithm is one of supervised linear dimensionality reduction methods. Its nonlinear version kernel discriminant neighborhood embedding (KDNE) is expected to behave well on classification tasks. However, since KDNE constructs an adjacent graph in the original space, the adjacency graph could not represent the adjacent information in the kernel mapping space. By introducing hidden space, this paper proposes a novel nonlinear method for DNE, called hidden space discriminant neighborhood embedding (HDNE). This algorithm first maps the data in the original space into a high dimensional hidden space by a set of nonlinear hidden functions, and then builds an adjacent graph incorporating neighborhood information of the dataset in the hidden space. Finally, DNE is used to find a transformation matrix which would map the data in the hidden space to a low-dimensional subspace. The proposed method is applied to ORL face and MNIST handwritten digit databases. Experimental results show that the proposed method is efficiency for classification tasks.
Coding practice is the most efficient way in learning of programming related courses. In this paper, we propose a programming related courses' E-learning platform based on online judge. This platform is designed a...
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For mining useful data from mass data generated by Internet of things, analyses shortages of the traditional Apriori algorithm which has a lower mining efficiency and occupies the larger memory space. So, MapReduce mo...
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In this paper, we propose an adaptive haze removal method based on dark channel prior. The dark channel value is compensated for estimating the transmission;the atmospheric light will be re-estimated through the analy...
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作者:
Du, LizhiCollege of Computer Science and Technology
Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430065 China
Algorithm studies on the Hamilton cycle are mainly based on the Rotation-Extension method developed by Posa. However, due to the deficiency of Posa's method, all these products are only efficient for much denser g...
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ISBN:
(纸本)9789881925336
Algorithm studies on the Hamilton cycle are mainly based on the Rotation-Extension method developed by Posa. However, due to the deficiency of Posa's method, all these products are only efficient for much denser graphs or sparse but regular graphs. By many years' study, we developed the "Enlarged Rotation-Extension" technique which utterly changed and expanded the Posa's original one and can surmount its deficiency. Based on this technique, our algorithm can quickly calculate randomly produced un-directed graphs with up to ten thousand vertices on personal computer, no matter dense or sparse, the correctness is one hundred percent. We also calculated the data of hamilton cycles on a famous web site and we still got one hundred percent correctness.
In this paper, we propose an augmented dependency-to-string model to combine the merits of both the head-dependents relations at handling long distance reordering and the fixed and floating structures at handling loca...
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ISBN:
(纸本)9781941643266
In this paper, we propose an augmented dependency-to-string model to combine the merits of both the head-dependents relations at handling long distance reordering and the fixed and floating structures at handling local reordering. For this purpose, we first compactly represent both the head-dependent relation and the fixed and floating structures into translation rules;second, in decoding we build "on-the-fly" new translation rules from the compact translation rules that can incorporate non-syntactic phrases into translations, thus alleviate the non-syntactic phrase coverage problem of dependency-to-string translation (Xie et al., 2011). Large-scale experiments on Chinese-to-English translation show that our augmented dependency-to-string model gains significant improvement of averaged +0.85 BLEU scores on three test sets over the dependencyto- string model.
The portrait region of portrait photo may be too dark due to backlight during the process of photo taking. Aiming at this problem, we propose a new image enhancement method based on histogram-based contrast method(HC)...
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The portrait region of portrait photo may be too dark due to backlight during the process of photo taking. Aiming at this problem, we propose a new image enhancement method based on histogram-based contrast method(HC) image saliency detection. Portrait area in portrait photo is the most salient region, thus we first use HC to measure saliency and then extract the portrait area from the portrait photo with the help of saliency map. We simply enhance the portrait region, keeping the background unchanged. Experiment results prove that proposed algorithm is able to compress the dynamic range of the original image and yield pleasing enhancement result.
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