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Search Results (21)
  • Open Access

    ARTICLE

    Deep Neural Network Driven Automated Underwater Object Detection

    Ajisha Mathias1, Samiappan Dhanalakshmi1,*, R. Kumar1, R. Narayanamoorthi2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5251-5267, 2022, DOI:10.32604/cmc.2022.021168 - 11 October 2021

    Abstract Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in… More >

  • Open Access

    ARTICLE

    A Mixture Model Parameters Estimation Algorithm for Inter-Contact Times in Internet of Vehicles

    Cheng Gong1,2, Xinzhu Yang1, Wei Huangfu3,4,*, Qinghua Lu5

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2445-2457, 2021, DOI:10.32604/cmc.2021.016713 - 21 July 2021

    Abstract Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles (IoV). Mixture models are appropriate to describe complex spatial-temporal data. By calculating the expectation of hidden variables in vehicle communication, Expectation Maximization (EM) algorithm solves the maximum likelihood estimation of parameters, and then obtains the mixture model of vehicle communication opportunities. However, the EM algorithm requires multiple iterations and each iteration needs to process all the data. Thus its computational complexity is high. A parameter estimation algorithm with low computational complexity based on Bin Count (BC) and Differential Evolution (DE) (PEBCDE)… More >

  • Open Access

    ARTICLE

    A Weighted Spatially Constrained Finite Mixture Model for Image Segmentation

    Mohammad Masroor Ahmed1,*, Saleh Al Shehri2, Jawad Usman Arshed3, Mahmood Ul Hassan4, Muzammil Hussain5, Mehtab Afzal6

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 171-185, 2021, DOI:10.32604/cmc.2021.014141 - 12 January 2021

    Abstract Spatially Constrained Mixture Model (SCMM) is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field (MAP-MRF). It developed its own maximization step to be used within this framework. This research has proposed an improvement in the SCMM’s maximization step for segmenting simulated brain Magnetic Resonance Images (MRIs). The improved model is named as the Weighted Spatially Constrained Finite Mixture Model (WSCFMM). To compare the performance of SCMM and WSCFMM, simulated T1-Weighted normal MRIs were segmented. A region of interest (ROI) was extracted from segmented images. The similarity level More >

  • Open Access

    ARTICLE

    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 - 07 January 2021

    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… More >

  • Open Access

    ARTICLE

    OPTIMIZATION STUDIES OF TRANSPIRATION COOLING USING POROUS MEDIUM WITH GRADUALLY-CHANGED STRUCTURE

    Yu Chena , Shen Dua, Dong Lia, Yang Gaob, Ya-Ling Hea,*

    Frontiers in Heat and Mass Transfer, Vol.15, pp. 1-13, 2020, DOI:10.5098/hmt.15.19

    Abstract Non-uniform heating and vapor blockage deteriorate the effectiveness of transpiration cooling, and an optimization method by using porous media with a gradually-changed structure is proposed. The numerical tool based on a two-phase mixture model and local thermal non-equilibrium assumption considering variable properties of coolant is applied. Porous media with linearly-changed porosity or particle diameter is analyzed. The transient results presented that the structure of gradually-changed porosity (or particle diameter) with appropriate parameters can delay the heat transfer deterioration. And it is confirmed that the present theoretical model is an effective tool for optimization design of More >

  • Open Access

    ARTICLE

    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 - 21 August 2020

    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… More >

  • Open Access

    ARTICLE

    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 - 06 August 2020

    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 More >

  • Open Access

    ARTICLE

    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 - 23 July 2020

    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 More >

  • Open Access

    ARTICLE

    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 More >

  • Open Access

    ARTICLE

    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… More >

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