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Electronics Engineering Perspectives on Computer Vision Applications: An Overview of Techniques, Sub-areas, Advancements and Future Challenges

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Cutting Edge Applications of Computational Intelligence Tools and Techniques

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1118))

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Abstract

This chapter provides a strategic overview of applications in the computer vision domain. We initially introduce the etymology of computer vision, main tasks, key techniques, and algorithms. Traditional feature extraction methods and deep learning techniques, including prominent algorithms like Region-Based Convolutional Neural Network (R-CNN) and You Only Look Once (YOLO), are explored. We discuss important sub-areas such as image classification, object detection, and image semantic segmentation. The versatility of computer vision is showcased, particularly in autonomous vehicles, healthcare, and surveillance. Furthermore, we delve into the challenges and potential of computer vision, highlighting the necessity for advanced algorithmic methodologies, efficient hardware, robust privacy protections, and conscientious ethical considerations. We also explore upcoming trends, including cross-modal learning, sophisticated ‘vision GPT’ models, and unified models that share architecture and parameters across different tasks. These future directions indicate a transformative impact across various sectors, encompassing autonomous driving, healthcare imaging, and e-commerce. Additionally, we outline the future challenges and trends in the field, underscoring the significance of continuous research and development to address issues such as data scarcity, model interpretability, and privacy concerns. By effectively addressing these challenges and capitalizing on emerging trends, computer vision stands poised to make profound advancements with far-reaching implications. This comprehensive overview aims to provide a solid foundation for understanding the field of computer vision and its potential impact across multiple industries and applications.

Yu Xun Zheng and K.-W. (G. H.) A. Chee—These authors contributed equally and share first authorship.

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Acknowledgements

Y. X. Zheng is greatly indebted to co-first author Professor K.-W. (G. H.) A. Chee for the conceptualization and the leading of the manuscript preparation, and the addition and editing of substantial technical content, data collation, and project supervision. This work was supported by the BK21 (Electronic Electric Convergence Talent Nurturing Education Research Center) funded by the Ministry of Education of Korea, the Dong-il Cultural Scholarship Foundation, and the Kyungpook National University Research Fund 2021.

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Authors and Affiliations

  1. Kyungpook National University, Daegu, 41566, Republic of Korea

    Yu Xun Zheng, K.-W. (G. H.) A. Chee, Anand Paul & Jeonghong Kim

  2. School of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 315100, China

    H. Lv

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  1. Yu Xun Zheng
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Correspondence to K.-W. (G. H.) A. Chee .

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Editors and Affiliations

  1. University of Detroit Mercy, Detroit, MI, USA

    Kevin Daimi

  2. Asia Pacific International College (APIC), Sydney, NSW, Australia

    Abeer Alsadoon

  3. Polytechnic Institute of Porto, Porto, Portugal

    Luis Coelho

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Zheng, Y.X., Chee, KW., Paul, A., Kim, J., Lv, H. (2023). Electronics Engineering Perspectives on Computer Vision Applications: An Overview of Techniques, Sub-areas, Advancements and Future Challenges. In: Daimi, K., Alsadoon, A., Coelho, L. (eds) Cutting Edge Applications of Computational Intelligence Tools and Techniques. Studies in Computational Intelligence, vol 1118. Springer, Cham. https://doi.org/10.1007/978-3-031-44127-1_6

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