Special lssues

Computer Vision and Machine Learning for Real-Time Applications

Submission Deadline: 09 March 2023 (closed)

Guest Editors

Dr. Khalil Khan, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Pakistan.
Dr. Jehad Ali, Ajou University, South Korea.
Dr. Ikram Syed, National University of Science and Technology, Pakistan.


Computer vision and machine learning have applications in almost all areas of Computer Science and Engineering. Due to recent developments in machine learning in general and deep learning in particular its applications are increased more. Our aim is to encourage scientists and researchers to publish their original results, covering applications in all branches of engineering and science. Some novel aspects of computer vision should be reported for real-world applications also validating the available datasets.


Pattern recognition
Image classification
Image understanding
Medical imaging
Deep learning and its applications in compute vision
Image retrieval
Image compression

Published Papers

  • Open Access


    Estimating Anthropometric Soft Biometrics: An Empirical Method

    Bilal Hassan, Hafiz Husnain Raza Sherazi, Mubashir Ali, Yusra Siddiqi
    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2727-2743, 2023, DOI:10.32604/iasc.2023.039275
    (This article belongs to the Special Issue: Computer Vision and Machine Learning for Real-Time Applications)
    Abstract Following the success of soft biometrics over traditional biometrics, anthropometric soft biometrics are emerging as candidate features for recognition or retrieval using an image/video. Anthropometric soft biometrics uses a quantitative mode of annotation which is a relatively better method for annotation than qualitative annotations adopted by traditional biometrics. However, one of the most challenging tasks is to achieve a higher level of accuracy while estimating anthropometric soft biometrics using an image or video. The level of accuracy is usually affected by several contextual factors such as overlapping body components, an angle from the camera, and… More >

  • Open Access


    Pre-Locator Incorporating Swin-Transformer Refined Classifier for Traffic Sign Recognition

    Qiang Luo, Wenbin Zheng
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2227-2246, 2023, DOI:10.32604/iasc.2023.040195
    (This article belongs to the Special Issue: Computer Vision and Machine Learning for Real-Time Applications)
    Abstract In the field of traffic sign recognition, traffic signs usually occupy very small areas in the input image. Most object detection algorithms directly reduce the original image to a specific size for the input model during the detection process, which leads to the loss of small object information. Additionally, classification tasks are more sensitive to information loss than localization tasks. This paper proposes a novel traffic sign recognition approach, in which a lightweight pre-locator network and a refined classification network are incorporated. The pre-locator network locates the sub-regions of the traffic signs from the original… More >

  • Open Access


    Pure Detail Feature Extraction Network for Visible-Infrared Re-Identification

    Jiaao Cui, Sixian Chan, Pan Mu, Tinglong Tang, Xiaolong Zhou
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2263-2277, 2023, DOI:10.32604/iasc.2023.039894
    (This article belongs to the Special Issue: Computer Vision and Machine Learning for Real-Time Applications)
    Abstract Cross-modality pedestrian re-identification has important applications in the field of surveillance. Due to variations in posture, camera perspective, and camera modality, some salient pedestrian features are difficult to provide effective retrieval cues. Therefore, it becomes a challenge to design an effective strategy to extract more discriminative pedestrian detail. Although many effective methods for detailed feature extraction are proposed, there are still some shortcomings in filtering background and modality noise. To further purify the features, a pure detail feature extraction network (PDFENet) is proposed for VI-ReID. PDFENet includes three modules, adaptive detail mask generation module (ADMG),… More >

  • Open Access


    Real-Time CNN-Based Driver Distraction & Drowsiness Detection System

    Abdulwahab Ali Almazroi, Mohammed A. Alqarni, Nida Aslam, Rizwan Ali Shah
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2153-2174, 2023, DOI:10.32604/iasc.2023.039732
    (This article belongs to the Special Issue: Computer Vision and Machine Learning for Real-Time Applications)
    Abstract Nowadays days, the chief grounds of automobile accidents are driver fatigue and distractions. With the development of computer vision technology, a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them, reducing accidents. This paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor vehicle. Employing the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network (CNN) any changes by focusing on the eyes and mouth zone, precision… More >

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