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

초록

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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

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