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  • Open Access


    Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

    Adéla Hamplová1,*, Alexey Lyavdansky2,*, Tomáš Novák1, Ondřej Svojše1, David Franc1, Arnošt Veselý1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2869-2889, 2024, DOI:10.32604/cmes.2024.050791

    Abstract This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions, employing two state-of-the-art deep learning algorithms, namely YOLOv8 and Roboflow 3.0. The goal is to contribute to the preservation and understanding of historical texts, showcasing the potential of modern deep learning methods in archaeological research. Our research culminates in several key findings and scientific contributions. We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context. We also created… More >

  • Open Access


    BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

    Xuejie Wang1, Jianxun Zhang1,*, Ye Tao2, Xiaoli Yuan1, Yifan Guo1

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4621-4639, 2024, DOI:10.32604/cmc.2024.051556

    Abstract While single-modal visible light images or infrared images provide limited information, infrared light captures significant thermal radiation data, whereas visible light excels in presenting detailed texture information. Combining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations, resulting in high-quality images with enhanced contrast and rich texture details. Such capabilities hold promising applications in advanced visual tasks including target detection, instance segmentation, military surveillance, pedestrian detection, among others. This paper introduces a novel approach, a dual-branch decomposition fusion network based on AutoEncoder (AE), which decomposes multi-modal features into intensity… More >

  • Open Access


    Braille Character Segmentation Algorithm Based on Gaussian Diffusion

    Zezheng Meng, Zefeng Cai, Jie Feng*, Hanjie Ma, Haixiang Zhang, Shaohua Li

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1481-1496, 2024, DOI:10.32604/cmc.2024.048002

    Abstract Optical braille recognition methods typically employ existing target detection models or segmentation models for the direct detection and recognition of braille characters in original braille images. However, these methods need improvement in accuracy and generalizability, especially in densely dotted braille image environments. This paper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithm based on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. This is applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining the central coordinates of More >

  • Open Access


    Detection of Student Engagement in E-Learning Environments Using EfficientnetV2-L Together with RNN-Based Models

    Farhad Mortezapour Shiri1,*, Ehsan Ahmadi2, Mohammadreza Rezaee1, Thinagaran Perumal1

    Journal on Artificial Intelligence, Vol.6, pp. 85-103, 2024, DOI:10.32604/jai.2024.048911

    Abstract Automatic detection of student engagement levels from videos, which is a spatio-temporal classification problem is crucial for enhancing the quality of online education. This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos. The evaluation of these models utilizes the DAiSEE dataset, a public repository capturing student affective states in e-learning scenarios. The initial model integrates EfficientNetV2-L with Gated Recurrent Unit (GRU) and attains an accuracy of 61.45%. Subsequently, the second model combines EfficientNetV2-L with bidirectional GRU (Bi-GRU), yielding More >

  • Open Access


    Multi-Branch High-Dimensional Guided Transformer-Based 3D Human Posture Estimation

    Xianhua Li1,2,*, Haohao Yu1, Shuoyu Tian1, Fengtao Lin3, Usama Masood1

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3551-3564, 2024, DOI:10.32604/cmc.2024.047336

    Abstract The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional (3D) method that takes into account self-occlusion, badly posedness, and a lack of depth data in the per-frame 3D posture estimation from two-dimensional (2D) mapping to 3D mapping. Firstly, by examining the relationship between the movements of different bones in the human body, four virtual skeletons are proposed to enhance the cyclic constraints of limb joints. Then, multiple parameters describing the skeleton are fused and projected into a high-dimensional space. Utilizing a multi-branch network, motion features between bones and overall motion More >

  • Open Access


    A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images

    Yuejie Li1,2,*, Shijun Li3

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3041-3070, 2024, DOI:10.32604/cmc.2023.045741

    Abstract 6G is envisioned as the next generation of wireless communication technology, promising unprecedented data speeds, ultra-low Latency, and ubiquitous Connectivity. In tandem with these advancements, blockchain technology is leveraged to enhance computer vision applications’ security, trustworthiness, and transparency. With the widespread use of mobile devices equipped with cameras, the ability to capture and recognize Chinese characters in natural scenes has become increasingly important. Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount, such as facial recognition or personal healthcare monitoring. Users can control their visual data and grant or revoke access as needed.… More >

  • Open Access


    Recent Advances on Deep Learning for Sign Language Recognition

    Yanqiong Zhang, Xianwei Jiang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2399-2450, 2024, DOI:10.32604/cmes.2023.045731

    Abstract Sign language, a visual-gestural language used by the deaf and hard-of-hearing community, plays a crucial role in facilitating communication and promoting inclusivity. Sign language recognition (SLR), the process of automatically recognizing and interpreting sign language gestures, has gained significant attention in recent years due to its potential to bridge the communication gap between the hearing impaired and the hearing world. The emergence and continuous development of deep learning techniques have provided inspiration and momentum for advancing SLR. This paper presents a comprehensive and up-to-date analysis of the advancements, challenges, and opportunities in deep learning-based sign… More >

  • Open Access


    Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

    Aysha Naseer1, Nouf Abdullah Almujally2, Saud S. Alotaibi3, Abdulwahab Alazeb4, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1381-1398, 2024, DOI:10.32604/cmc.2023.042963

    Abstract Object segmentation and recognition is an imperative area of computer vision and machine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their features. The proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks (ANNs). The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label them based on their characteristics. Then, two distinct kinds of features are obtained from the segmented images More >

  • Open Access


    Deep Learning Approach for Hand Gesture Recognition: Applications in Deaf Communication and Healthcare

    Khursheed Aurangzeb1, Khalid Javeed2, Musaed Alhussein1, Imad Rida3, Syed Irtaza Haider1, Anubha Parashar4,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 127-144, 2024, DOI:10.32604/cmc.2023.042886

    Abstract Hand gestures have been used as a significant mode of communication since the advent of human civilization. By facilitating human-computer interaction (HCI), hand gesture recognition (HGRoc) technology is crucial for seamless and error-free HCI. HGRoc technology is pivotal in healthcare and communication for the deaf community. Despite significant advancements in computer vision-based gesture recognition for language understanding, two considerable challenges persist in this field: (a) limited and common gestures are considered, (b) processing multiple channels of information across a network takes huge computational time during discriminative feature extraction. Therefore, a novel hand vision-based convolutional neural network… More >

  • Open Access


    Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques

    Raveena Selvanarayanan1, Surendran Rajendran1,*, Youseef Alotaibi2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 759-782, 2024, DOI:10.32604/cmes.2023.044084

    Abstract Colletotrichum kahawae (Coffee Berry Disease) spreads through spores that can be carried by wind, rain, and insects affecting coffee plantations, and causes 80% yield losses and poor-quality coffee beans. The deadly disease is hard to control because wind, rain, and insects carry spores. Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93% accuracy using a random forest method. If the dataset is too small and noisy, the algorithm may not learn data patterns and generate accurate predictions.… More >

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