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  • Open Access

    PROCEEDINGS

    In-Silico Automated 3D Reconstruction of the Biomechanical Trapeziometacarpal Joint from 4D Imaging

    Yen-Jen Lai1, I-Ling Chang1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012918

    Abstract Biomechanical research reveals that the geometric shapes and dynamic behaviors of organ tissues play a pivotal role in determining their mechanical properties. Recent advancements in time-correlated imaging technologies, such as Computed Tomography (4D-CT) and Magnetic Resonance Imaging (4D-MRI), have enabled the non-invasive capture of both geometric data and dynamic information over time. However, the manual segmentation of these extensive datasets proves to be laborious and expensive. This study introduces an automated workflow designed for image segmentation and classification within 4D-CT scans, with a specific focus on the bone structures surrounding the Trapeziometacarpal (TMC) joint in More >

  • Open Access

    ARTICLE

    Development of Multi-Agent-Based Indoor 3D Reconstruction

    Hoi Chuen Cheng, Frederick Ziyang Hong, Babar Hussain, Yiru Wang, Chik Patrick Yue*

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 161-181, 2024, DOI:10.32604/cmc.2024.053079 - 15 October 2024

    Abstract Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling technologies. This work contributes to a framework addressing localization, coordination, and vision processing for multi-agent reconstruction. A system architecture fusing visible light positioning, multi-agent path finding via reinforcement learning, and 360° camera techniques for 3D reconstruction is proposed. Our visible light positioning algorithm leverages existing lighting for centimeter-level localization without additional infrastructure. Meanwhile, a decentralized reinforcement learning approach is developed to solve the multi-agent path finding problem, with communications among agents optimized. Our 3D reconstruction pipeline utilizes equirectangular projection from 360° cameras to More >

  • Open Access

    ARTICLE

    Modeling Method of C/C-ZrC Composites and Prediction of Equivalent Thermal Conductivity Tensor Based on Asymptotic Homogenization

    Junpeng Lyu1, Hai Mei1,2, Liping Zu1, Lisheng Liu1,2,*, Liangliang Chu1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 391-410, 2024, DOI:10.32604/cmes.2023.030614 - 22 September 2023

    Abstract This article proposes a modeling method for C/C-ZrC composite materials. According to the superposition of Gaussian random field, the original gray model is obtained, and the threshold segmentation method is used to generate the C-ZrC inclusion model. Finally, the fiber structure is added to construct the microstructure of the three-phase plain weave composite. The reconstructed inclusions can meet the randomness of the shape and have a uniform distribution. Using an algorithm based on asymptotic homogenization and finite element method, the equivalent thermal conductivity prediction of the microstructure finite element model was carried out, and the… More >

  • Open Access

    ARTICLE

    Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

    Shariar Md Imtiaz1, Ki-Chul Kwon1, F. M. Fahmid Hossain1, Md. Biddut Hossain1, Rupali Kiran Shinde1, Sang-Keun Gil2, Nam Kim1,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2921-2937, 2023, DOI:10.32604/csse.2023.040205 - 09 November 2023

    Abstract This article describes a novel approach for enhancing the three-dimensional (3D) point cloud reconstruction for light field microscopy (LFM) using U-net architecture-based fully convolutional neural network (CNN). Since the directional view of the LFM is limited, noise and artifacts make it difficult to reconstruct the exact shape of 3D point clouds. The existing methods suffer from these problems due to the self-occlusion of the model. This manuscript proposes a deep fusion learning (DL) method that combines a 3D CNN with a U-Net-based model as a feature extractor. The sub-aperture images obtained from the light field… More >

  • Open Access

    ARTICLE

    Easy to Calibrate: Marker-Less Calibration of Multiview Azure Kinect

    Sunyoung Bu1, Suwon Lee2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 3083-3096, 2023, DOI:10.32604/cmes.2023.024460 - 09 March 2023

    Abstract Reconstructing a three-dimensional (3D) environment is an indispensable technique to make augmented reality and augmented virtuality feasible. A Kinect device is an efficient tool for reconstructing 3D environments, and using multiple Kinect devices enables the enhancement of reconstruction density and expansion of virtual spaces. To employ multiple devices simultaneously, Kinect devices need to be calibrated with respect to each other. There are several schemes available that calibrate 3D images generated from multiple Kinect devices, including the marker detection method. In this study, we introduce a markerless calibration technique for Azure Kinect devices that avoids the More > Graphic Abstract

    Easy to Calibrate: Marker-Less Calibration of Multiview Azure Kinect

  • Open Access

    ARTICLE

    Improved Lightweight Deep Learning Algorithm in 3D Reconstruction

    Tao Zhang1,*, Yi Cao2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5315-5325, 2022, DOI:10.32604/cmc.2022.027083 - 21 April 2022

    Abstract The three-dimensional (3D) reconstruction technology based on structured light has been widely used in the field of industrial measurement due to its many advantages. Aiming at the problems of high mismatch rate and poor real-time performance caused by factors such as system jitter and noise, a lightweight stripe image feature extraction algorithm based on You Only Look Once v4 (YOLOv4) network is proposed. First, Mobilenetv3 is used as the backbone network to effectively extract features, and then the Mish activation function and Complete Intersection over Union (CIoU) loss function are used to calculate the improved More >

  • Open Access

    CORRECTION

    Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoTEnhanced Smart Cities

    Jing Zhang1,2, Xin Qi3,*, San Hlaing Myint3, Zheng Wen4

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2809-2809, 2021, DOI:10.32604/cmc.2021.17410 - 26 July 2021

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    A 3D Measurement Method Based on Coded Image

    Jinxing Niu1,*, Yayun Fu1, Qingsheng Hu1, Shaojie Yang1, Tao Zhang1, Sunil Kumar Jha2

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1839-1849, 2021, DOI:10.32604/cmc.2021.017797 - 21 July 2021

    Abstract The binocular stereo vision system is often used to reconstruct 3D point clouds of an object. However, it is challenging to find effective matching points in two object images with similar color or less texture. This will lead to mismatching by using the stereo matching algorithm to calculate the disparity map. In this context, the object can’t be reconstructed precisely. As a countermeasure, this study proposes to combine the Gray code fringe projection with the binocular camera as well as to generate denser point clouds by projecting an active light source to increase the texture… More >

  • Open Access

    ARTICLE

    Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoT-Enhanced Smart Cities

    Jing Zhang1,2, Xin Qi3,*, San Hlaing Myint3, Zheng Wen4

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2807-2824, 2021, DOI:10.32604/cmc.2021.017410 - 13 April 2021

    Abstract With increasingly more smart cameras deployed in infrastructure and commercial buildings, 3D reconstruction can quickly obtain cities’ information and improve the efficiency of government services. Images collected in outdoor hazy environments are prone to color distortion and low contrast; thus, the desired visual effect cannot be achieved and the difficulty of target detection is increased. Artificial intelligence (AI) solutions provide great help for dehazy images, which can automatically identify patterns or monitor the environment. Therefore, we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning. First, we propose a… More >

  • Open Access

    ARTICLE

    Three-Dimensional Measurement Using Structured Light Based on Deep Learning

    Tao Zhang1,*, Jinxing Niu1, Shuo Liu1, Taotao Pan1, Brij B. Gupta2,3

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 271-280, 2021, DOI:10.32604/csse.2021.014181 - 23 December 2020

    Abstract Three-dimensional (3D) reconstruction using structured light projection has the characteristics of non-contact, high precision, easy operation, and strong real-time performance. However, for actual measurement, projection modulated images are disturbed by electronic noise or other interference, which reduces the precision of the measurement system. To solve this problem, a 3D measurement algorithm of structured light based on deep learning is proposed. The end-to-end multi-convolution neural network model is designed to separately extract the coarse- and fine-layer features of a 3D image. The point-cloud model is obtained by nonlinear regression. The weighting coefficient loss function is introduced More >

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