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Emerging Frontiers in Computer Vision: A Critical Analysis of Deep Learning Techniques and Their Real-World Applications
Issue Vol. 2 No. 02 (2025): Volume 02 Issue 02 --- Section Articles
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Abstract
Deep learning has become the cornerstone of modern computer vision, fundamentally transforming how machines perceive and interpret the visual world. This article presents a critical review of the key deep learning techniques that have driven this revolution. We trace the evolution of foundational concepts, from early neural networks to the sophisticated convolutional neural network (CNN) architectures that dominate the field today. The article is structured to provide a comprehensive overview, beginning with an introduction to the core concepts and historical context of deep learning in computer vision. We then delve into the methodologies, systematically examining influential architectures and techniques for major computer vision tasks, including image classification, object detection, semantic segmentation, and image restoration. Subsequently, we evaluate the performance and results of these methods, highlighting their groundbreaking impact on various application scenarios, from medical imaging to autonomous systems. Finally, we discuss the broader implications, current challenges such as the creation of deepfakes, and promising future directions for research and development. By synthesizing a wide array of seminal and contemporary works, this review offers a detailed landscape of the field, providing valuable insights for both new and experienced researchers.
Keywords
References
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