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Search Results (17)
  • Open Access


    Identifying Cross Section Technology Application through Chinese Patent Analysis

    Ping-Yu Hsu1, Ming-Shien Cheng2,*, Chih-Hao Wen3, Yen-Huei Ko1

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 269-285, 2021, DOI:10.32604/iasc.2021.013404

    Abstract Cross-domain technology application is the application of technology from one field to another to create a wide range of application opportunities. To successfully identify emerging technological application cross sections of patent documents is vital to the competitive advantage of companies, and even nations. An automatic process is needed to save precious resources of human experts and exploit huge numbers of patent documents. Chinese patent documents are the source data of our experiment. In this study, an identification algorithm was developed on the basis of a cross-collection mixture model to identify cross section and emerging technology from patents written in Chinese.… More >

  • Open Access


    Threshold-Based Adaptive Gaussian Mixture Model Integration (TA-GMMI) Algorithm for Mapping Snow Cover in Mountainous Terrain

    Yonghong Zhang1,2, Guangyi Ma1,2,*, Wei Tian3, Jiangeng Wang4, Shiwei Chen1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1149-1165, 2020, DOI:10.32604/cmes.2020.010932

    Abstract Snow cover is an important parameter in the fields of computer modeling, engineering technology and energy development. With the extensive growth of novel hardware and software compositions creating smart, cyber physical systems’ (CPS) efficient end-to-end workflows. In order to provide accurate snow detection results for the CPS’s terminal, this paper proposed a snow cover detection algorithm based on the unsupervised Gaussian mixture model (GMM) for the FY-4A satellite data. At present, most snow cover detection algorithms mainly utilize the characteristics of the optical spectrum, which is based on the normalized difference snow index (NDSI) with thresholds in different wavebands. These… More >

  • Open Access


    Image Denoising Based on the Asymmetric Gaussian Mixture Model

    Ke Jin, Shunfeng Wang*

    Journal on Internet of Things, Vol.2, No.1, pp. 1-11, 2020, DOI:10.32604/jiot.2020.09071

    Abstract In recent years, image restoration has become a huge subject, and finite hybrid model has been widely used in image denoising because of its easy modeling and strong explanatory results. The gaussian mixture model is the most common one. The existing image denoising methods usually assume that each component of the natural image is subject to the gaussian mixture model (GMM). However, this approach is not entirely reasonable. It is well known that most natural images are complex and their distribution is not entirely gaussian. As a result, there are still many problems that GMM cannot solve. This paper tries… More >

  • Open Access


    Robust Core Tensor Dictionary Learning with Modified Gaussian Mixture Model for Multispectral Image Restoration

    Leilei Geng1, Chaoran Cui1, Qiang Guo1, Sijie Niu2, Guoqing Zhang3, Peng Fu4, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 913-928, 2020, DOI:10.32604/cmc.2020.09975

    Abstract The multispectral remote sensing image (MS-RSI) is degraded existing multispectral camera due to various hardware limitations. In this paper, we propose a novel core tensor dictionary learning approach with the robust modified Gaussian mixture model for MS-RSI restoration. First, the multispectral patch is modeled by three-order tensor and high-order singular value decomposition is applied to the tensor. Then the task of MS-RSI restoration is formulated as a minimum sparse core tensor estimation problem. To improve the accuracy of core tensor coding, the core tensor estimation based on the robust modified Gaussian mixture model is introduced into the proposed model by… More >

  • Open Access


    Automatic Delineation of Lung Parenchyma Based on Multilevel Thresholding and Gaussian Mixture Modelling

    S. Gopalakrishnan1, *, A. Kandaswamy2

    CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.2, pp. 141-152, 2018, DOI:10.3970/cmes.2018.114.141

    Abstract Delineation of the lung parenchyma in the thoracic Computed Tomography (CT) is an important processing step for most of the pulmonary image analysis such as lung volume extraction, lung nodule detection and pulmonary vessel segmentation. An automatic method for accurate delineation of lung parenchyma in thoracic Computed Tomography images is presented in this paper. The proposed method involves a segmentation phase followed by a lung boundary correction technique. The tissues in the thoracic Computed Tomography can be represented by a number of Gaussians. We propose a histogram utilized Adaptive Multilevel Thresholding (AMT) for estimating the total number of Gaussians and… More >

  • Open Access


    Multidirectional Gaussian Mixture Models for Nonlinear Uncertainty Propagation

    V. Vittaldev1, R. P. Russell2

    CMES-Computer Modeling in Engineering & Sciences, Vol.111, No.1, pp. 83-117, 2016, DOI:10.3970/cmes.2016.111.083

    Abstract Monte Carlo simulations are an accurate but computationally expensive procedure for approximating the resultant non-Gaussian probability density function (PDF) after propagation of an initial Gaussian PDF through a nonlinear function. Univariate splitting libraries for Gaussian Mixture Models (GMMs) exist with up to five elements in the literature. The number of splits are extended in the present work by generating three homoscedastic univariate splitting libraries with up to 39 elements. Mulitvariate GMMs are typically handled with splits along a single direction. Instead, we generate a regular multidirectional grid over the initial multivariate Gaussian distribution by recursively applying the splitting library along… More >

  • Open Access


    Texture Segmentation based on Multivariate Generalized Gaussian Mixture Model

    K. Naveen Kumar1, K. Srinivasa Rao2, Y. Srinivas3, Ch. Satyanarayana4

    CMES-Computer Modeling in Engineering & Sciences, Vol.107, No.3, pp. 201-221, 2015, DOI:10.3970/cmes.2015.107.201

    Abstract Texture Analysis is one of the prime considerations for image analysis and processing. Texture segmentation gained lot of importance due to its ready applicability in automation of scene identification and computer vision. Several texture segmentation methods have been developed and analysed with the assumption that the feature vector associated with the texture of the image region is modelled as Gaussian mixture model. Due to the limitations of the Gaussian model being meso kurtic, it may not characterise the texture of all image regions accurately. Hence in this paper, a texture segmentation algorithm is developed and analysed with the assumption that… More >

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