Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-01-28 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the texture synthesis in new computer graphics technical conditions, this paper proposes a texture interpolation method to synthesize different resolution textures adaptively from a single real world texture. The synthesized texture’s DPI is same as the original texture example. Firstly, the proposed method use a high-dimensional interpolation algorithm to generate an intermediate guidance texture with target resolution by split the source texture. Secondly, particular random patches are selected to fill the gaps in the intermediate texture according to the self-similarity in the source texture. Finally, these patches are seamlessly embedded in the previous intermediate texture by the Poisson image-editing algorithm. Experimental results show that the proposed method can handle both stationary and non-stationary texture synthesis, and the synthetic results are more consistent in the visual properties of the source texture. In addition, the implement logic of the proposed method is enough simple to be programing and executing on a common mobile platforms.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-11-29 Cooperative journals: 《计算机应用研究》
Abstract： This paper proposed a construction framework of pet knowledge graph. It designed and constructed the schema layer in a top-down manner and constructed the data layer by extracting knowledge from semi-structured and unstructured data. For entity extraction of unstructured data, this paper proposed a symptom-named entity recognition method which combined conditional random field (CRF) and pet symptom dictionary. The method used symptom dictionary to identify the text and obtain the semantic category information, and then combined CRF and the semantic information to identify symptom-named entities. The experimental results showed the effectiveness of the method. The attribute graph model supported by the OrientDB database was selected for knowledge representation. The knowledge graph used the OrientDB graph database for knowledge storage. In addition, examples were shown for the constructed pet knowledge graph.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-24 Cooperative journals: 《计算机应用研究》
Abstract： Since the existing principal component analysis(PCA) of symbolic data mostly use some representative information instead of symbolic data, a histogram principal component analysis is proposed. Represent a histogram data by a quantile function with its characteristic, and introduce the Wasserstein distance which fully takes into account the probability distribution of the histogram data. It is easy to obtain the covariance matrix to perform the principal component analysis using this distance. However, the eigenvectors corresponding to the first m largest eigenvalues obtained by this method is not necessarily negative, so it cannot guarantee that the principal components are also quantile functions when they are represented by the quantile functions. For this point, combining the idea of DSD (distribution and symmetric distribution) regression model studied by Dias [1]et al, defining the corresponding symmetric distribution variables for each histogram variable, then obtain the non-negative principal component coefficients with the generalized covariance matrix. The experiments show the effectiveness of the algorithm. Besides, this method overcomes the disadvantage that the PCA coefficient of the histogram in [2] may be negative and retains more information of the original data.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-18 Cooperative journals: 《计算机应用研究》
Abstract： Researched on the task dividing method based on the architecture of the network emulation for the fusion of virtualization and digital simulation to improve the performance. This method took into account the advantages of virtualization and digital simulation, and the emulation network topology was divided into virtualization topology area and digital simulation topology area, and then divided the two topology area combined with given physical resources aiming at load balancing and remote traffic minimizing. Extensive experiments showed that using the method to divide the network emulation task, the remote traffic was reduced by 33.7%, 25.1% averagely, and the degree of load balancing was improved by 56.3%, 38.0% averagely, compared with the random algorithm and the uniform load balancing algorithm. The task dividing method can effectively reduce the remote traffic and improve the degree of load balancing.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-24 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the problem that the spatial influence factors are easily misidentified by noise in the fuzzy C-means algorithm (WFLICM) for local spatial information, this paper proposed a fuzzy C-means clustering algorithm for image segmentation (NLWFLICM) based on local and non-local spatial information. It introduced the non-local spatial information into the fuzzy influencing factor of WFLICM algorithm, the weight of local and non-local information is adaptively set according to the noise level, and the fuzzy influence factors of the central point are re-marked. The experimental results show that the NLWFLICM algorithm is more robust and adaptive than the WFLICM algorithm, and improves the robustness of the WFLICM algorithm to a large extent, while preserving the image texture. In order to improve the clustering performance and convergence speed of the algorithm, combined with the advantages of Canopy algorithm for fast clustering of data, this paper proposes an improved algorithm for FCM image segmentation based on Canopy clustering and non-local spatial information (Canopy-NLWFLICM) before clustering algorithm. This can improve the convergence speed and image segmentation accuracy. Key words: clustering algorithm ; Canopy algorithm ; fuzzy C-means cluster; local and non-local spatial information#12;
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