sis 21(32): e4

Research Article

Machine Learning in Computer Vision: A Review

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  • @ARTICLE{10.4108/eai.21-4-2021.169418,
        author={Abdullah Ayub Khan and Asif Ali Laghari and Shafique Ahmed Awan},
        title={Machine Learning in Computer Vision: A Review},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        keywords={Machine Learning, Computer Vision, Supervised and Unsupervised Learning, Medical Imaging, Pattern Recognition, Feature Extraction, Neural Network},
  • Abdullah Ayub Khan
    Asif Ali Laghari
    Shafique Ahmed Awan
    Year: 2021
    Machine Learning in Computer Vision: A Review
    DOI: 10.4108/eai.21-4-2021.169418
Abdullah Ayub Khan1,2, Asif Ali Laghari1,*, Shafique Ahmed Awan2
  • 1: Faculty of Computer Science, Sindh Madressatul Islam University, Karachi, Sindh, Pakistan
  • 2: Faculty of Computing Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, Pakistan
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INTRODUCTION: Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human. The scenario of manual feeding of the machine is changed in the modern era, it will learn automatically. Supervised and unsupervised ML techniques are used as a distinct purpose like feature extraction, pattern recognition, object detection, and classification.

OBJECTIVES: In Computer Vision (CV), ML performs a significant role to extract crucial information from images. CV successfully contributes to multiple domains, surveillance system, optical character recognition, robotics, suspect detection, and many more. The direction of CV research is going toward healthcare realm, medical imaging (MI) is the emerging technology, play a vital role to enhance image quality and recognized critical features of binary medical image, covert original image into grayscale and set the threshold values for segmentation.

CONTRIBUTION: This paper will address the importance of machine learning, state-of-the-art, and how ML is utilized in computer vision and image processing. This survey will provide details about the type of tools and applications, datasets, and techniques. Limitations of previous work and challenges of future work also discussed. Further, we identify and discuss a set of open issues yet to be addressed, for efficiently applying of ML in Computer vision and image process.

METHODS, RESULTS, AND CONCLUSION: In this review paper, we have discussed the techniques and various types of supervised and unsupervised algorithms of ML, general overview of image processing and the results based on the impact; neural network enabled models, limitations, tools and application of CV, moreover, highlight the critical open research areas of ML in CV.