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  • Open Access

    ARTICLE

    Aggregate Point Cloud Geometric Features for Processing

    Yinghao Li1,2,3, Renbo Xia1,2,*, Jibin Zhao1,2,*, Yueling Chen1,2, Liming Tao1,2,3, Hangbo Zou1,2,3, Tao Zhang1,2,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 555-571, 2023, DOI:10.32604/cmes.2023.024470

    Abstract As 3D acquisition technology develops and 3D sensors become increasingly affordable, large quantities of 3D point cloud data are emerging. How to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be solved. The point cloud geometric information is hidden in disordered, unstructured points, making point cloud analysis a very challenging problem. To address this problem, we propose a novel network framework, called Tree Graph Network (TGNet), which can sample, group, and aggregate local geometric features. Specifically, we construct a Tree Graph by explicit rules, which consists of curves extending in all… More >

  • Open Access

    ARTICLE

    Self-Care Assessment for Daily Living Using Machine Learning Mechanism

    Mouazma Batool1, Yazeed Yasin Ghadi2, Suliman A. Alsuhibany3, Tamara al Shloul4, Ahmad Jalal1, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1747-1764, 2022, DOI:10.32604/cmc.2022.025112

    Abstract Nowadays, activities of daily living (ADL) recognition system has been considered an important field of computer vision. Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders. Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth (distance information) and visual cues has greatly enhanced the performance of activity recognition. In this paper, an RGB-D-based ADL recognition system has been presented. Initially, human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene.… More >

  • Open Access

    ARTICLE

    Computerized Detection of Limbal Stem Cell Deficiency from Digital Cornea Images

    Hanan A. Hosni Mahmoud*, Doaa S. Khafga, Amal H. Alharbi

    Computer Systems Science and Engineering, Vol.40, No.2, pp. 805-821, 2022, DOI:10.32604/csse.2022.019633

    Abstract Limbal Stem Cell Deficiency (LSCD) is an eye disease that can cause corneal opacity and vascularization. In its advanced stage it can lead to a degree of visual impairment. It involves the changing in the semispherical shape of the cornea to a drooping shape to downwards direction. LSCD is hard to be diagnosed at early stages. The color and texture of the cornea surface can provide significant information about the cornea affected by LSCD. Parameters such as shape and texture are very crucial to differentiate normal from LSCD cornea. Although several medical approaches exist, most of them requires complicated procedure… More >

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