<![CDATA[Computer vision has become one of the most critical components in the advancement of artificial intelligence, enabling machines not only to perceive but also to interpret the world around them. This paper explores the central role of computer vision in bridging the gap between visual perception and higher-level machine understanding. By integrating deep learning, pattern recognition, and semantic interpretation, computer vision transforms raw visual data into structured knowledge that supports decision-making, reasoning, and autonomous behavior. The discussion highlights recent progress in image recognition, object detection, scene understanding, and multimodal learning, emphasizing how these innovations drive AI toward more human-like cognition. Furthermore, the paper addresses the challenges of scalability, generalization, and ethical implications, offering insights into future directions for research and applications.]]>
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