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  • Open Access


    Shadow detection and correction using a combined 3D GIS and image processing approach

    Safa Ridene1 , Reda Yaagoubi1, Imane Sebari1, Audrey Alajouanine2

    Revue Internationale de Géomatique, Vol.29, No.3, pp. 241-253, 2019, DOI:10.3166/rig.2019.00091

    Abstract While shadow can give useful information about size and shape of objects, it can pose problems in feature detection and object detection, thereby, it represents one of the major perturbator phenomenons frequently occurring on images and unfortunately, it is inevitable. “Shadows may lead to the failure of image analysis processes and also cause a poor quality of information which in turn leads to problems in implementation of algorithms.” (Mahajan and Bajpayee, 2015). It also affects multiple image analysis applications, whereby shadow cast by buildings deteriorate the spectral values of the surfaces. Therefore, its presence causes a deterioration in the visual… More >

  • Open Access


    Modeling for application data with 3D spatiale feature in MADS

    Chamseddine Zaki1 , Mohamed Ayet1,2, Allah Bilel Soussi2

    Revue Internationale de Géomatique, Vol.29, No.3, pp. 255-262, 2019, DOI:10.3166/rig.2019.00086

    Abstract A conceptual spatiotemporal data model must be able to offer users a semantic richness of expression to meet their diverse needs concerning the modeling of spatio-temporal data. The conceptual spatiotemporal data model must be able to represent the objects, relationships and events that can occur in a field of study, track data history, support the multirepresentation of these data, and represent temporal and spatial data with two and three dimensions features. The model must also allow the assignment of different types of constraints to relations and provide a complete orthogonality between dimensions and concepts. The MADS model meets several requirements… More >

  • Open Access


    A Three-Dimensional Model for the Formation Pressure in Wellbores under Uncertainty

    Jiawei Zhang*, Qing Wang, Hongchun Huang, Haige Wang, Guodong Ji, Meng Cui, Hongyuan Zhang

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.9, pp. 2305-2314, 2023, DOI:10.32604/fdmp.2023.026304

    Abstract Formation pressure is the key parameter for the analysis of wellbore safety. With increasing drilling depth, however, the behavior of this variable becomes increasingly complex. In this work, a 3D model of the formation pressure under uncertainty is presented. Moreover a relevant algorithm is elaborated. First, the logging data of regional key drilling wells are collected and a one-dimensional formation pressure profile along the well depth is determined. Then, a 3D model of regional formation pressure of the hierarchical group layer is defined by using the Kriging interpolation algorithm relying on a support vector machine (SVM) and the formation pressure… More >

  • Open Access


    A Systematic Literature Review of Deep Learning Algorithms for Segmentation of the COVID-19 Infection

    Shroog Alshomrani*, Muhammad Arif, Mohammed A. Al Ghamdi

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5717-5742, 2023, DOI:10.32604/cmc.2023.038059

    Abstract Coronavirus has infected more than 753 million people, ranging in severity from one person to another, where more than six million infected people died worldwide. Computer-aided diagnostic (CAD) with artificial intelligence (AI) showed outstanding performance in effectively diagnosing this virus in real-time. Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients. This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs. We used the methodology of systematic reviews and meta-analyses (PRISMA) flow method. This research aims… More >

  • Open Access


    MFF-Net: Multimodal Feature Fusion Network for 3D Object Detection

    Peicheng Shi1,*, Zhiqiang Liu1, Heng Qi1, Aixi Yang2

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5615-5637, 2023, DOI:10.32604/cmc.2023.037794

    Abstract In complex traffic environment scenarios, it is very important for autonomous vehicles to accurately perceive the dynamic information of other vehicles around the vehicle in advance. The accuracy of 3D object detection will be affected by problems such as illumination changes, object occlusion, and object detection distance. To this purpose, we face these challenges by proposing a multimodal feature fusion network for 3D object detection (MFF-Net). In this research, this paper first uses the spatial transformation projection algorithm to map the image features into the feature space, so that the image features are in the same spatial dimension when fused… More >

  • Open Access


    Robust Watermarking Algorithm for Medical Volume Data Based on Polar Cosine Transform and 3D-DCT

    Pengju Zhang1, Jingbing Li1,2,*, Uzair Aslam Bhatti1,2, Jing Liu3, Yen-wei Chen4, Dekai Li1, Lei Cao1

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5853-5870, 2023, DOI:10.32604/cmc.2023.036462

    Abstract The amount of 3D data stored and transmitted in the Internet of Medical Things (IoMT) is increasing, making protecting these medical data increasingly prominent. However, there are relatively few researches on 3D data watermarking. Moreover, due to the particularity of medical data, strict data quality should be considered while protecting data security. To solve the problem, in the field of medical volume data, we proposed a robust watermarking algorithm based on Polar Cosine Transform and 3D-Discrete Cosine Transform (PCT and 3D-DCT). Each slice of the volume data was transformed by PCT to obtain feature row vector, and then the reshaped… More >

  • Open Access


    3D Human Pose Estimation Using Two-Stream Architecture with Joint Training

    Jian Kang1, Wanshu Fan1, Yijing Li2, Rui Liu1, Dongsheng Zhou1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 607-629, 2023, DOI:10.32604/cmes.2023.024420

    Abstract With the advancement of image sensing technology, estimating 3D human pose from monocular video has become a hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequent action analysis and understanding. It empowers a wide spectrum of potential applications in various areas, such as intelligent transportation, human-computer interaction, and medical rehabilitation. Currently, some methods for 3D human pose estimation in monocular video employ temporal convolutional network (TCN) to extract inter-frame feature relationships, but the majority of them suffer from insufficient inter-frame feature relationship extractions. In this paper, we decompose the 3D joint location regression… More >

  • Open Access


    Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

    Amarendra Reddy Panyala1,2, M. Baskar3,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3883-3899, 2023, DOI:10.32604/csse.2023.037050

    Abstract The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey mass, texture, and shape are… More >

  • Open Access


    An Improved Calibration Method of Grating Projection Measurement System

    Qiucheng Sun*, Weiyu Dai, Mingyu Sun, Zeming Ren, Mingze Wang

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3957-3970, 2023, DOI:10.32604/cmc.2023.037254

    Abstract In the traditional fringe projection profilometry system, the projector and the camera light center are both spatially virtual points. The spatial position relationships specified in the model are not easy to obtain, leading to inaccurate system parameters and affecting measurement accuracy. This paper proposes a method for solving the system parameters of the fringe projection profilometry system, and the spatial position of the camera and projector can be adjusted in accordance with the obtained calibration parameters. The steps are as follows: First, in accordance with the conversion relationship of the coordinate system in the calibration process, the calculation formula of… More >

  • Open Access


    Dual Branch PnP Based Network for Monocular 6D Pose Estimation

    Jia-Yu Liang1, Hong-Bo Zhang1,*, Qing Lei2, Ji-Xiang Du3, Tian-Liang Lin4

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3243-3256, 2023, DOI:10.32604/iasc.2023.035812

    Abstract Monocular 6D pose estimation is a functional task in the field of computer vision and robotics. In recent years, 2D-3D correspondence-based methods have achieved improved performance in multiview and depth data-based scenes. However, for monocular 6D pose estimation, these methods are affected by the prediction results of the 2D-3D correspondences and the robustness of the perspective-n-point (PnP) algorithm. There is still a difference in the distance from the expected estimation effect. To obtain a more effective feature representation result, edge enhancement is proposed to increase the shape information of the object by analyzing the influence of inaccurate 2D-3D matching on… More >

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