by David Gatidis (translated into English by Rebekka Eick)
Definition & classification
Computer Vision (CV), which can be translated to “Bildverstehen” (“image comprehension”) or “maschinelles (Computer-) Sehen” (“machine (computer-) vision”) in German, describes a field of development of artificial intelligence.  Its focus is the mechanical development of visual perception. It is therefore connected to the field of machine vision. Wikipedia describes CV as an “interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.“  For Benjamin Bratton, Professor of Visual Arts at the University of California, the task of CV is to identify differences and patterns from thousands of similar images and copies with their digital information, and to determine their authenticity and originality.  In research, image information is ordered and compared using CV.
The technology CV is used to try to transmit an optimized human vision to the computer (the machine) and to perfect it. It is possible for machines to filter out details and similarities using algorithms and to use them to classify images into categories, for example, or to recognize and characterize human facial and emotional expressions.
This can be useful in various areas, such as digital art collections. Images can be compared based on multiple categories such as brightness, color, subject, or pattern. The online service “EyeEM” offers software that organizes images and assigns a score, based on which the images are displayed in the user’s gallery: “The score should be like an aesthetic assessment of the image that decides how far up the picture appears in the topic gallery. “ 
Ultimately, CV combines a biological point of view (enabling computer-based vision) with a developer point of view (development of an intelligent system that relieves us of tasks). 
The story of CV began in the 1960s at MIT, where Larry Roberts first tried to extract 3D information from 2D perspectives. Over the decades, CV, always in connection with other research fields such as image processing or computer graphics, has consolidated its role as an important research field in the development of artificial intelligence and machine learning. 
Range of Application
Today CV is mainly used in the industrial sector, but also in the fields of medical technology and automation. It is used in everyday life, as applications and cameras installed in modern smartphones are equipped with facial recognition software. CV is used in surveillance cameras, to unlock screens and to get access to various password-protected areas. Using digital scanning methods, algorithms can recognize thousands of details of the human face as well as facial expressions and gestures, age and gender. Autonomous driving is another very large area of application for CV. Self-driving vehicles can already participate autonomously in road traffic and, with the help of the Lidar method, react to potential hazards or obstacles, like red traffic lights or traffic signs and interruptions. Thanks to machine learning, the knowledge gained can be passed on and increased.  The motor vehicle takes on the task of the driver because it not only moves independently, but can also interact with its environment.
In addition to and as a result of mentioned advantages and developments of CV, questions do come up. To what extent do such methods intervene in our private life (especially face ID and surveillance) and how will the use of CV and AI shape social life in the future?  Furthermore, many algorithms are not yet fully developed, so that in the context of autonomous driving, for instance, incomplete measurements can lead to accidents. Problems can also occur with face recognition. In 2015, a devastating bugin Google’s facial recognition software was discovered when people with dark skin were classified as gorillas and the question arose whether algorithms could be racist. 
 Cf. de.wikipedia.org/wiki/Maschinelles_Sehen (Accessed: 21.02.2019)
 Bratton, Benjamin H.: Notes for “The work of the image in the age of machine vision“ In: Salemi, Mohammed (2016): For Machine Use Only: Contemplations on Algorithmic Epis-temology, New York.
 Weimer, Marco: Die neue App von EyeEm soll Deine Smartphone-Bilder aufräumen. In: gruenderszene.de/allgemein/the-roll-eyeem-app (Accessed: 03.03.2019).
 Huang, T. S.: Computer Vision: Evolution and Promise. At: cds.cern.ch/record/400313/files/p21.pdf(Accessed: 18.02.2019).
 Jee, Charlotte: Crowdsourcing für Straßenschilder. At: heise.de/tr/artikel/Crowdsourcing-fuer-Strassenschilder-4311975.html (Accessed: 20.02.2019).
 Rieder, Bernhard & Röhle, Theo: Digital Methods. From Challenges to Bildung. In: Schäfer, Tobias & van Es, Karin:The Datafied Society: Studying Culture through Data, Amsterdam (2017).
 Kühl, Eike: “Meine Freundin ist kein Gorilla“.At: zeit.de/digital/internet/2015-07/google-fotos-algorithmus-rassismus (03.03.2019).