Computerized information extraction from images

Computer vision

Computer vision

Computer vision tasks include methods for <a href="/en/Image_sensor" title="Image sensor" class="wl">acquiring</a>, <a href="/en/Image_processing" title="Image processing" class="mw-redirect wl">processing</a>, <a href="/en/Image_analysis" title="Image analysis" class="wl">analyzing</a> and understanding digital images, and extraction of <a href="/en/High-dimensional" title="High-dimensional" class="mw-redirect wl">high-dimensional</a> data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions.<a href="#cite_note-Klette-2014-1" style="counter-reset: mw-Ref 1;">[1]</a><a href="#cite_note-Shapiro-Stockman-2001-2" style="counter-reset: mw-Ref 2;">[2]</a><a href="#cite_note-Morris-2004-3" style="counter-reset: mw-Ref 3;">[3]</a><a href="#cite_note-Jahne-Haussecker-2000-4" style="counter-reset: mw-Ref 4;">[4]</a> Understanding in this context means the transformation of visual images (the input of 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.

<div class="shortdescription nomobile noexcerpt noprint searchaux" style="display:none" about="#mwt1">Computerized information extraction from images</div>

The <a href="/en/Scientific_discipline" title="Scientific discipline" class="mw-redirect wl">scientific discipline</a> of computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, or medical scanning devices. The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems.

Sub-domains of computer vision include <a href="/en/3D_reconstruction" title="3D reconstruction" class="wl">scene reconstruction</a>, <a href="/en/Object_detection" title="Object detection" class="wl">object detection</a>, event detection, <a href="/en/Video_tracking" title="Video tracking" class="wl">video tracking</a>, <a href="/en/Object_recognition" title="Object recognition" class="mw-redirect wl">object recognition</a>, <a href="/en/3D_pose_estimation" title="3D pose estimation" class="wl">3D pose estimation</a>, learning, indexing, <a href="/en/Motion_estimation" title="Motion estimation" class="wl">motion estimation</a>, <a href="/en/Visual_servoing" title="Visual servoing" class="wl">visual servoing</a>, 3D scene modeling, image generation, and <a href="/en/Image_restoration" title="Image restoration" class="wl">image restoration</a>. 

Adopting computer vision technology might be painstaking for organizations as there is no single point solution for it. There are very few companies that provide a unified and distributed platform or an Operating System where computer vision applications can be easily deployed and managed.

Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images 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. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, or medical scanning devices. The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems. Sub-domains of computer vision include scene reconstruction, object detection, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual servoing, 3D scene modeling, image generation, and image restoration.  