Editing Computer vision (section)

{{Short description|Computerized information extraction from images}}
{{Artificial intelligence|Major goals}}
'''Computer vision''' tasks include methods for [[image sensor|acquiring]], [[Image processing|processing]], [[Image analysis|analyzing]], and understanding [[digital image]]s, and extraction of [[high-dimensional]] data from the real world in order to produce numerical or symbolic information, e.g. in the form of decisions.<ref name="Klette-2014" /><ref name="Shapiro-Stockman-2001" /><ref name="Morris-2004" /><ref name="Jahne-Haussecker-2000" /> "Understanding" in this context signifies the transformation of visual images (the input to the [[retina]]) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

The [[scientific discipline]] of computer vision is concerned with the theory behind artificial systems that extract information from images. Image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a [[3D scanning|3D scanner]], 3D point clouds from [[Lidar|LiDaR]] sensors, or medical scanning devices. The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems.

Subdisciplines of computer vision include [[3D reconstruction|scene reconstruction]], [[object detection]], [[event detection]], [[activity recognition]], [[video tracking]], [[object recognition]], [[3D pose estimation]], learning, indexing, [[motion estimation]], [[visual servoing]], [[3D modeling|3D scene modeling]], and [[Digital photograph restoration|image restoration]].