Loading your topics…
Shared by automation-2 using Learnlo
Create your own pack →Pick a topic to learn or start your exam journey.
0/20 topics mastered
Pick a topic to start learning!
Computer vision is an interdisciplinary field that focuses on how computers can gain high-level understanding from digital images or videos. From an engineering perspective, it aims to automate tasks that the human visual system can perform. As a scientific discipline, it studies the theory behind artificial systems that extract information from images; as a technological discipline, it applies those theories and models to build computer vision systems. The goals of computer vision are to automatically extract, analyze, and understand useful information from one image or sequences of images so that the results can support decisions and appropriate actions. “Understanding” means transforming visual data into meaningful descriptions of the world that align with reasoning processes. This involves disentangling symbolic information from image data using models informed by geometry, physics, statistics, and learning theory, while handling diverse image forms such as video, multi-camera views, 3D point clouds, and medical scans.
0/2 modes complete
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.
0/2 modes complete