An important aim in pattern recognition is to cluster the given shapes. This paper presents a shape recognition and retrieval algorithm. The algorithm first extracts the skeletal features using the medial axis transfo...
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An important aim in pattern recognition is to cluster the given shapes. This paper presents a shape recognition and retrieval algorithm. The algorithm first extracts the skeletal features using the medial axis transform. Then, the features are transformed into a string of symbols with the similarity among those symbols computed based on the edit distance. Finally, the shapes are identified using dynamic programming. Two public datasets are analyzed to demonstrate that the present approach is better than previous approaches.
In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. Howe...
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In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), can significantly reduce the class separation problem. Furthermore, maximizing the harmonic mean of Kullback-Leibler (KL) divergences of class pairs (MHMD) emphasizes smaller divergences more than MGMD, and deals with the class separation problem more effectively. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate labeled data. To take advantage of unlabeled data for subspace selection, semi-supervised MHMD (SSMHMD) is proposed using graph Laplacian as normalization. Quasi-Newton method is adopted to solve the optimization problem. Experiments on synthetic data and real image data show the validity of SSMHMD.
In order to decrease dispersion penalty and increase the optical bandwidth efficiency,an optical single-side-band modulation(SSBM) scheme in sub-carrier multiplexing(SCM) is *** principle of the SSBM is analytically p...
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In order to decrease dispersion penalty and increase the optical bandwidth efficiency,an optical single-side-band modulation(SSBM) scheme in sub-carrier multiplexing(SCM) is *** principle of the SSBM is analytically presented,and a configuration for generating optical SSB signal is proposed using a balanced Mach-Zehnder electro-optic modulator.
Risk evaluation is very important to the design and improvement of physical protection systems. In this paper, an evaluation method of multi-source information fusion is proposed based on the D-S evidence theory. In t...
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Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In this work, a discrete structure representation ...
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Detection of shot transitions servers as the preliminary step to video indexing and retrieval. Locally linear embedding (LLE) algorithm fails when it is applied to video with multi-shot. In this paper, we present a ...
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ISBN:
(纸本)9781424450015
Detection of shot transitions servers as the preliminary step to video indexing and retrieval. Locally linear embedding (LLE) algorithm fails when it is applied to video with multi-shot. In this paper, we present a novel framework of shot transitions detection. The method involves two processes: First we extract the manifold feature of shot transition using LLE through addition of virtual frames on an enriched set, and then they are classified by *** show that the recognition rate of shot transition is reached over 90%.
In this paper, a novel method for predicting RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS) is proposed. In RNATS, two search models, intensification search and diversif...
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In this paper, a novel method for predicting RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS) is proposed. In RNATS, two search models, intensification search and diversification search, are designed to exploit the local regions around the current solution and explore the unvisited space, respectively. Simulation experiments are conducted for six RNA sequences to show that the proposed method is feasible and effective.
In this paper we review the major approaches to nonrigid object reconstruction based on multi-view images. It tries to reflect the profile of this area by focusing more on those subjects that have been given more impo...
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In this paper we review the major approaches to nonrigid object reconstruction based on multi-view images. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context most of the paper is devoted to present all kinds of approaches for non-rigid object reconstruction based on multiview images. A number of references are provided that describe applications of non-rigid object reconstruction based on multiview images The paper ends by addressing some important issues and open questions that can be subject of future research.
Risk evaluation is very important to the design and improvement of physical protection systems. In this paper, an evaluation method of multi-source information fusion is proposed based on the D-S evidence theory. In t...
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ISBN:
(纸本)9781424471645;9781424471638
Risk evaluation is very important to the design and improvement of physical protection systems. In this paper, an evaluation method of multi-source information fusion is proposed based on the D-S evidence theory. In the proposed method, each individual component of the protection system in the simulated plane is modeled. Then, the threat report of each component according to the specific tactics is determined based on its real environment. Finally, the comprehensive threat distribution is obtained based on through the D-S evidence theory to combine multi-sources information. The proposed method can easily applied to the evaluation of the effectiveness of the protection system. We make the total threat of the protection system lowest through changes of the protection resources allocation. A numerical example is used to illustrate the efficiency of the proposed method.
In this paper, a novel algorithm for image classification is presented which uses the projective value of adjacency spectrum as classified samples. Firstly, the eigenvalues of adjacency matrices constructed on the fea...
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In this paper, a novel algorithm for image classification is presented which uses the projective value of adjacency spectrum as classified samples. Firstly, the eigenvalues of adjacency matrices constructed on the feature point-sets of images are obtained by singular value decomposition. Secondly, the eigenvalues are projected onto the eigenspace by means of the covariance matrix. Finally, image classification is performed by adopting RBF and PNN neural networks as classifiers respectively. Meanwhile, some theoretical analyses are given to support the proposed method.
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