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  • Open Access


    Point Cloud Based Semantic Segmentation Method for Unmanned Shuttle Bus

    Sidong Wu, Cuiping Duan, Bufan Ren, Liuquan Ren, Tao Jiang, Jianying Yuan*, Jiajia Liu, Dequan Guo

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2707-2726, 2023, DOI:10.32604/iasc.2023.038948

    Abstract The complexity of application scenarios and the enormous volume of point cloud data make it difficult to quickly and effectively segment the scenario only based on the point cloud. In this paper, to address the semantic segmentation for safety driving of unmanned shuttle buses, an accurate and effective point cloud-based semantic segmentation method is proposed for specified scenarios (such as campus). Firstly, we analyze the characteristic of the shuttle bus scenarios and propose to use ROI selection to reduce the total points in computation, and then propose an improved semantic segmentation model based on Cylinder3D, which improves mean Intersection over… More >

  • Open Access


    Relevant Visual Semantic Context-Aware Attention-Based Dialog

    Eugene Tan Boon Hong1, Yung-Wey Chong1,*, Tat-Chee Wan1, Kok-Lim Alvin Yau2

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2337-2354, 2023, DOI:10.32604/cmc.2023.038695

    Abstract The existing dataset for visual dialog comprises multiple rounds of questions and a diverse range of image contents. However, it faces challenges in overcoming visual semantic limitations, particularly in obtaining sufficient context from visual and textual aspects of images. This paper proposes a new visual dialog dataset called Diverse History-Dialog (DS-Dialog) to address the visual semantic limitations faced by the existing dataset. DS-Dialog groups relevant histories based on their respective Microsoft Common Objects in Context (MSCOCO) image categories and consolidates them for each image. Specifically, each MSCOCO image category consists of top relevant histories extracted based on their semantic relationships… More >

  • Open Access


    An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images

    Jing Xin1,*, Kenan Du1, Jiale Feng1, Mao Shan2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2621-2640, 2023, DOI:10.32604/cmes.2023.027467

    Abstract This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images. The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance. To address these issues, we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model. Then, an indoor RGB-D image semantic segmentation network is proposed, which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud model. Finally, Bayesian updating is… More >

  • Open Access


    Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

    Ghoroub Talal Bostaji*, Emad Sami Jaha

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1571-1591, 2023, DOI:10.32604/csse.2023.039701

    Abstract Human recognition technology based on biometrics has become a fundamental requirement in all aspects of life due to increased concerns about security and privacy issues. Therefore, biometric systems have emerged as a technology with the capability to identify or authenticate individuals based on their physiological and behavioral characteristics. Among different viable biometric modalities, the human ear structure can offer unique and valuable discriminative characteristics for human recognition systems. In recent years, most existing traditional ear recognition systems have been designed based on computer vision models and have achieved successful results. Nevertheless, such traditional models can be sensitive to several unconstrained… More >

  • Open Access


    RO-SLAM: A Robust SLAM for Unmanned Aerial Vehicles in a Dynamic Environment

    Jingtong Peng*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2275-2291, 2023, DOI:10.32604/csse.2023.039272

    Abstract When applied to Unmanned Aerial Vehicles (UAVs), existing Simultaneous Localization and Mapping (SLAM) algorithms are constrained by several factors, notably the interference of dynamic outdoor objects, the limited computing performance of UAVs, and the holes caused by dynamic objects removal in the map. We proposed a new SLAM system for UAVs in dynamic environments to solve these problems based on ORB-SLAM2. We have improved the Pyramid Scene Parsing Network (PSPNet) using Depthwise Separable Convolution to reduce the model parameters. We also incorporated an auxiliary loss function to supervise the hidden layer to enhance accuracy. Then we used the improved PSPNet… More >

  • Open Access


    An Optimized Chinese Filtering Model Using Value Scale Extended Text Vector

    Siyu Lu1, Ligao Cai1, Zhixin Liu2, Shan Liu1, Bo Yang1, Lirong Yin3, Mingzhe Liu4, Wenfeng Zheng1,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1881-1899, 2023, DOI:10.32604/csse.2023.034853

    Abstract With the development of Internet technology, the explosive growth of Internet information presentation has led to difficulty in filtering effective information. Finding a model with high accuracy for text classification has become a critical problem to be solved by text filtering, especially for Chinese texts. This paper selected the manually calibrated Douban movie website comment data for research. First, a text filtering model based on the BP neural network has been built; Second, based on the Term Frequency-Inverse Document Frequency (TF-IDF) vector space model and the doc2vec method, the text word frequency vector and the text semantic vector were obtained… More >

  • Open Access


    Anatomical Feature Segmentation of Femur Point Cloud Based on Medical Semantics

    Xiaozhong Chen*

    Molecular & Cellular Biomechanics, Vol.20, No.1, pp. 1-14, 2023, DOI:10.32604/mcb.2022.026964

    Abstract Feature segmentation is an essential phase for geometric modeling and shape processing in anatomical study of human skeleton and clinical digital treatment of orthopedics. Due to various degrees of freedom of bone surface, the existing segmentation algorithms can hardly meet specific medical need. To address this, a novel segmentation methodology for anatomical features of femur model based on medical semantics is put forward. First, anatomical reference objects (ARO) are created to represent typical characteristics of femur anatomy by 3D point fitting in combination with medical priori knowledge. Then, local point clouds between adjacent anatomies are selected according to the AROs… More >

  • Open Access


    A Hybrid Attention-Based Residual Unet for Semantic Segmentation of Brain Tumor

    Wajiha Rahim Khan1, Tahir Mustafa Madni1, Uzair Iqbal Janjua1, Umer Javed2, Muhammad Attique Khan3, Majed Alhaisoni4, Usman Tariq5, Jae-Hyuk Cha6,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.039188

    Abstract Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets. However, 3D networks can be computationally expensive and require significant training resources. This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy. The proposed model, called Hybrid Attention-Based Residual Unet (HA-RUnet), is based on the Unet architecture and utilizes residual blocks to extract low-… More >

  • Open Access


    Unsupervised Log Anomaly Detection Method Based on Multi-Feature

    Shiming He1, Tuo Deng1, Bowen Chen1, R. Simon Sherratt2, Jin Wang1,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 517-541, 2023, DOI:10.32604/cmc.2023.037392

    Abstract Log anomaly detection is an important paradigm for system troubleshooting. Existing log anomaly detection based on Long Short-Term Memory (LSTM) networks is time-consuming to handle long sequences. Transformer model is introduced to promote efficiency. However, most existing Transformer-based log anomaly detection methods convert unstructured log messages into structured templates by log parsing, which introduces parsing errors. They only extract simple semantic feature, which ignores other features, and are generally supervised, relying on the amount of labeled data. To overcome the limitations of existing methods, this paper proposes a novel unsupervised log anomaly detection method based on multi-feature (UMFLog). UMFLog includes… More >

  • Open Access


    ResCD-FCN: Semantic Scene Change Detection Using Deep Neural Networks

    S. Eliza Femi Sherley1,*, J. M. Karthikeyan1, N. Bharath Raj1, R. Prabakaran2, A. Abinaya1, S. V. V. Lakshmi3

    Journal on Artificial Intelligence, Vol.4, No.4, pp. 215-227, 2022, DOI:10.32604/jai.2022.034931

    Abstract Semantic change detection is extension of change detection task in which it is not only used to identify the changed regions but also to analyze the land area semantic (labels/categories) details before and after the timelines are analyzed. Periodical land change analysis is used for many real time applications for valuation purposes. Majority of the research works are focused on Convolutional Neural Networks (CNN) which tries to analyze changes alone. Semantic information of changes appears to be missing, there by absence of communication between the different semantic timelines and changes detected over the region happens. To overcome this limitation, a… More >

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