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| Title | Unveiling The Vision: A Comprehensive Review Of Computer Vision In Ai And Ml |
|---|---|
| ID_Doc | 59794 |
| Authors | Laad M.; Maurya R.; Saiyed N. |
| Year | 2024 |
| Published | 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems, ADICS 2024 |
| DOI | http://dx.doi.org/10.1109/ADICS58448.2024.10533631 |
| Abstract | In the era of rapid technological advancement, Computer Vision has emerged as a transformative force, reshaping the landscape of Artificial Intelligence (AI) and Machine Learning (ML). This comprehensive review paper aims to delve into the intricate evolution, methodologies, applications, challenges, and future trajectories of Computer Vision. Moving beyond a mere exploration of technical intricacies, our objective is to present a holistic narrative that encapsulates the profound impact of computer Vision on AI and ML and its repercussions across society. The journey begins by traversing the philosophical and historical roots of Computer Vision, unraveling the threads that connect human visual perception to the development of artificial vision. By exploring the historical evolution from early image processing to the current era of deep learning, we seek to elucidate the intellectual milestones that have shaped the field. Methodologically, this paper navigates through both traditional approaches and contemporary deep learning paradigms. It dissects traditional methods, emphasizing their enduring relevance and influence on modern Computer Vision applications. In parallel, exploring deep learning delves into established architectures all the nuanced impact of design choices on interpretability and explain ability. Applications form a cornerstone of our review, with an enriched focus on case studies that spotlight the transformative influence of Computer Vision. Beyond the traditional domains of image recognition, we delve into the healthcare renaissance, where Computer Vision contributes to diagnostics, drug discovery, and personalized medicine. Furthermore, we explore its role in smart cities, extending beyond surveillance to urban planning, traffic management, and environmental monitoring. © 2024 IEEE. |
| Author Keywords | Approaches; Artificial Intelligence; Computer vision; Deep Learning; Historical Department; Interpretability; Machine Learning; Methodologies; Traditional |
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