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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 form of decisions.

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 technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems. Subdisciplines of computer vision include scene reconstruction, object detection, event detection, activity recognition, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual servoing, 3D scene modeling, and image restoration.

Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. "Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding." As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. In more recent times, the terms computer vision and machine vision have converged to a greater degree.

What distinguished computer vision from the prevalent field of digital image processing at that time was a desire to extract three-dimensional structure from images with the goal of achieving full scene understanding. Studies in the 1970s formed the early foundations for many of the computer vision algorithms that exist today, including extraction of edges from images, labeling of lines, non-polyhedral and polyhedral modeling, representation of objects as interconnections of smaller structures, optical flow, and motion estimation.

Also, some of the learning-based methods developed within computer vision (e.g. neural net and deep learning based image and feature analysis and classification) have their background in neurobiology. Some strands of computer vision research are closely related to the study of biological vision—indeed, just as many strands of AI research are closely tied with research into human intelligence and the use of stored knowledge to interpret, integrate, and utilize visual information. Computer vision, on the other hand, develops and describes the algorithms implemented in software and hardware behind artificial vision systems.

Information about the environment could be provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot

The fields most closely related to computer vision are image processing, image analysis and machine vision. Computer graphics produces image data from 3D models, and computer vision often produces 3D models from image data. Computer vision includes 3D analysis from 2D images. Photogrammetry also overlaps with computer vision, e.g., stereophotogrammetry vs. computer stereo vision.

The computer vision and machine vision fields have significant overlap. Examples of applications of computer vision include systems for:

A second application area in computer vision is in industry, sometimes called machine vision, where information is extracted for the purpose of supporting a production process.

The classical problem in computer vision, image processing, and machine vision is that of determining whether or not the image data contains some specific object, feature, or activity.

Algorithms for Image Processing and Computer Vision (2nd ed.). Feature Extraction and Image Processing for Computer Vision (4th ed.).

Computer vision papers on the web – a complete list of papers of the most relevant computer vision conferences. Computer Vision Online Archived 2011-11-30 at the Wayback Machine – news, source code, datasets and job offers related to computer vision Computer Vision Container, Joe Hoeller GitHub: Widely adopted open-source container for GPU accelerated computer vision applications.

Quick Facts

• The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems.
• The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images.
• Subdisciplines of computer vision include scene reconstruction, object detection, event detection, activity recognition, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual servoing, 3D scene modeling, and image restoration.
• The fields most closely related to computer vision are image processing, image analysis and machine vision.
• Computer graphics produces image data from 3D models, and computer vision often produces 3D models from image data.

Source material: Wikipedia - "Computer vision". Adapted and summarized for DiscoverScroll. Original contributors are credited through the linked Wikipedia article. Read original on Wikipedia. CC BY-SA 4.0. Changes were made from the original.