Special Issues
Table of Content

Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision

Submission Deadline: 31 July 2024 View: 205 Submit to Special Issue

Guest Editors

Prof. Dr. Ahmad Taher Azar, Prince Sultan University, Saudi Arabia; Benha University, Egypt
Prof. Dr. Asadullah Shaikh, Najran University, Najran 61441, Saudi Arabia
Prof. Dr. Ibrahim A. Hameed, Norwegian University of Science and Technology, Norway

Summary

In the realm of contemporary technology, the evolution of artificial intelligence (AI) has had a profound impact on various domains, with one of the most notable being image processing and computer vision. As the digital age continues to redefine our world, the importance of harnessing AI techniques for image analysis and interpretation becomes increasingly evident. This special issue, titled "Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision," is a testament to the ever-growing significance of AI in the field, shedding light on the cutting-edge developments and innovations that are shaping the future of image processing and computer vision.

 

Artificial Intelligence has revolutionized the way we perceive and understand images, enabling machines to replicate human-like visual recognition and interpretation. This special issue aims to explore the latest trends, breakthroughs, and challenges in the integration of AI technologies for image processing and computer vision. By delving into the intricacies of these disciplines, we seek to offer a comprehensive perspective on how AI is redefining the boundaries of what is possible in fields like medical imaging, autonomous vehicles, surveillance, facial recognition, and beyond.

 

The scope of this special issue is as diverse as the applications of AI in image processing and computer vision themselves. We will delve into the methods, algorithms, and models that are propelling the field forward, as well as the ethical and societal considerations that arise with such powerful technologies. From deep learning techniques to the integration of AI with IoT devices and the challenges of interpretability and fairness, this collection of articles brings together experts and researchers from around the world to provide insights into the state-of-the-art AI solutions and the directions in which these technologies are headed.

 

This special issue serves as a testament to the continued growth and development of AI in these domains, and we hope that it inspires further research and collaboration to unlock the full potential of artificial intelligence for the analysis and understanding of visual data. We hope that this special issue serves as a valuable resource for researchers, practitioners, policymakers, and anyone interested in its scope.


Keywords

Artificial Intelligence
Computer Vision
Deep Learning
Edge Computing
Feature Extraction
Image Analysis
Machine Learning
Object Recognition
Pattern Recognition
Robotics
Image Enhancement
Image Segmentation
Image Processing
Visual Perception
Neural Networks
Autonomous Systems
Control Systems
Sensor Fusion
Human-Machine Interaction
Augmented Reality

Published Papers


  • Open Access

    ARTICLE

    Enhancing Communication Accessibility: UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

    Khushal Das, Fazeel Abid, Jawad Rasheed, Kamlish, Tunc Asuroglu, Shtwai Alsubai, Safeeullah Soomro
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.051335
    (This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
    Abstract Deaf people or people facing hearing issues can communicate using sign language (SL), a visual language. Many works based on rich source language have been proposed; however, the work using poor resource language is still lacking. Unlike other SLs, the visuals of the Urdu Language are different. This study presents a novel approach to translating Urdu sign language (UrSL) using the UrSL-CNN model, a convolutional neural network (CNN) architecture specifically designed for this purpose. Unlike existing works that primarily focus on languages with rich resources, this study addresses the challenge of translating a sign language… More >

  • Open Access

    ARTICLE

    Two-Layer Attention Feature Pyramid Network for Small Object Detection

    Sheng Xiang, Junhao Ma, Qunli Shang, Xianbao Wang, Defu Chen
    CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2024.052759
    (This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
    Abstract Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection. However, small objects are difficult to detect accurately because they contain less information. Many current methods, particularly those based on Feature Pyramid Network (FPN), address this challenge by leveraging multi-scale feature fusion. However, existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers, leading to suboptimal small object detection. To address this problem, we propose the Two-layer Attention Feature Pyramid Network (TA-FPN), featuring two key modules: the Two-layer Attention Module (TAM) and the… More >

    Graphic Abstract

    Two-Layer Attention Feature Pyramid Network for Small Object Detection

  • Open Access

    ARTICLE

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

    Awais Khan, Chomyong Kim, Jung-Yeon Kim, Ahsan Aziz, Yunyoung Nam
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1729-1755, 2024, DOI:10.32604/cmes.2024.049618
    (This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
    Abstract Sleep posture surveillance is crucial for patient comfort, yet current systems face difficulties in providing comprehensive studies due to the obstruction caused by blankets. Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns. Consequently, this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification, thereby enhancing the analysis of body position and comfort. This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras, which depict six commonly adopted postures: supine,… More >

    Graphic Abstract

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

  • Open Access

    ARTICLE

    Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

    Jing-Doo Wang, Chayadi Oktomy Noto Susanto
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1711-1728, 2024, DOI:10.32604/cmes.2024.048955
    (This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
    Abstract A significant obstacle in intelligent transportation systems (ITS) is the capacity to predict traffic flow. Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately. However, accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors. This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory (Conv-BiLSTM) with attention mechanisms. Prior studies neglected to include data pertaining to factors such as holidays, weather conditions, and More >

  • Open Access

    ARTICLE

    An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

    Md. Abdur Rahim, Fahmid Al Farid, Abu Saleh Musa Miah, Arpa Kar Puza, Md. Nur Alam, Md. Najmul Hossain, Sarina Mansor, Hezerul Abdul Karim
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1689-1710, 2024, DOI:10.32604/cmes.2024.048714
    (This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
    Abstract Handwriting is a unique and significant human feature that distinguishes them from one another. There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification. However, such systems are susceptible to forgery, posing security risks. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures… More >

  • Open Access

    ARTICLE

    A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

    Wei Wu, Yuan Zhang, Yunpeng Li, Chuanyang Li, Yan Hao
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 537-555, 2024, DOI:10.32604/cmes.2024.049174
    (This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
    Abstract Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities. Additionally, it leverages inter-modal correlation to enhance recognition performance. Concurrently, the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features. Nevertheless, two issues persist in multi-modal feature fusion recognition: Firstly, the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities. Secondly, during modal fusion, improper weight selection diminishes the salience of crucial modal features, thereby diminishing the overall recognition performance. To address these two issues, we introduce an… More >

    Graphic Abstract

    A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

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