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Computer vision converts images or image sequences into useful descriptions, measurements, classifications, and actions.
Image understanding and useful information extraction are central goals of computer vision. They involve acquiring, processing, analyzing, and interpreting images or video to transform visual data into meaningful numerical or symbolic information, such as object identities, scene descriptions, measurements, or decisions. This process uses concepts from geometry, physics, statistics, machine learning, and learning theory to infer the structure and content of the real world. A typical computer vision system performs image acquisition, preprocessing, feature extraction, detection or segmentation, high-level interpretation, recognition, and decision-making. It may extract edges, textures, shapes, motion, depth, objects, scenes, or events from data captured by cameras, 3D scanners, LiDAR, medical scanners, radar, or other sensors. Applications include industrial inspection, medical diagnosis, robotics, autonomous vehicles, security, navigation, and image restoration. Image-understanding systems organize information at low, intermediate, and high levels, progressing from basic visual primitives to surfaces, objects, scenes, and events.
Computer vision converts images or image sequences into useful descriptions, measurements, classifications, and actions.
Image understanding relies on models from geometry, physics, statistics, optimization, and machine learning.
Typical processing stages include image acquisition, preprocessing, feature extraction, detection or segmentation, high-level interpretation, recognition, and decision-making.
Useful information can include edges, textures, shapes, motion, depth, object identity, object pose, scene structure, and events.
Image-understanding systems operate at low-level, intermediate-level, and high-level abstractions, from image primitives to objects, scenes, and events.
Image restoration improves the reliability of information extraction by reducing noise, blur, and other forms of image degradation.
Computer vision supports applications such as medical imaging, industrial inspection, robotic navigation, autonomous vehicles, military systems, and security.
An interdisciplinary field that develops methods for automatically extracting, analyzing, and understanding useful information from images and videos.
The transformation of visual data into meaningful descriptions of objects, scenes, events, or actions.
The process of collecting visual or related sensor data using cameras, scanners, LiDAR, radar, or other imaging devices.
The preparation of image data through operations such as noise reduction, contrast enhancement, resampling, and scale adjustment.
The identification of informative visual characteristics such as edges, corners, textures, shapes, and motion.
The division of an image or video into meaningful regions, objects, or foreground and background areas.
The classification or identification of a detected image region as a particular object or category.
The alignment, comparison, or combination of different images or views of the same object or scene.
The recovery or improvement of degraded images by reducing noise, blur, interference, or other distortions.
The stage that interprets extracted data to estimate properties, verify hypotheses, recognize objects, or make application-specific decisions.
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