Computer Vision

Computer Vision is a field of Artificial Intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects, and then react to what they "see."

Core Concepts of Computer Vision

• Definition: Computer Vision is the science of making computers see and interpret the visual world as humans do.
• Techniques: It relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze, and understand images, they can capture images or videos in real time and interpret their surroundings.

Key Technologies in Computer Vision

• Image Recognition: The ability to identify objects, places, people, writing, and actions in images. Mobile photo apps like Google Photos implement this technology to tag and categorize images automatically.
• Object Detection: This is the technique of identifying and locating objects within images. It is used in various applications such as self-driving cars where pedestrian and obstacle detection is crucial.
• Facial Recognition: A form of computer vision that involves identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database.

Applications of Computer Vision

• Automotive Industry: Used in autonomous vehicles to navigate safely by identifying objects around them.
• Retail: In retail, computer vision algorithms are used to track inventories and analyze consumer behavior through surveillance cameras.
• Healthcare: It helps in diagnosing diseases by analyzing images from medical scans with accuracy that rivals or exceeds human experts.
• Agriculture: Farmers use computer vision to monitor crop health and manage farms more efficiently.

Computer Vision is increasingly becoming central to all artificial intelligence systems and applications, including robotics, automation, and what is traditionally considered AI.