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The Image-Theories behind Computer Vision

Abstract : Every computer vision algorithm is a set of instructions for how to decode images - in other words, a programmatic way of seeing. Every such algorithm therefore, explicitly or otherwise, embodies a particular understanding of theories, philosophies and ideologies of vision. Since the 1980s, computer vision scientists have chiefly referred to David Marr’s Vision (1982), itself an account of human visual neuroscience from the perspective of information theory. Other perspectives were toyed with by the scientific community, such as James Gibson’s Ecological Approach to Visual Perception (1979); the choice is axiomatic, and its implications for computer vision are totalizing, as they have to do not only with technical solutions but with the research problems being posited in the first place. This paper has two ambitions. Firstly, to uncover the tacit visual theories implicit in computer vision systems that might be used by humanities researchers. And secondly, that the very relationship between a theory of vision and its algorithmic implementation might be the start of a new intersection between computer vision and the history of visual culture: where we prototype computer vision systems based on historical theories of vision. These serve not as tools to aid the digestion of masses of visual material, but rather as algorithmic thought-experiments for exploring the intellectual history of vision.
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Contributor : Mathieu Schellenberger Connect in order to contact the contributor
Submitted on : Wednesday, September 22, 2021 - 3:15:00 PM
Last modification on : Thursday, September 23, 2021 - 3:27:41 AM


  • HAL Id : hal-03351749, version 1



Leonardo Impett. The Image-Theories behind Computer Vision. DHNord 2020 : The measurement of images. Computational approaches in the history and theory of the arts, Maison Européenne des Sciences de l'Homme et de la Société (MESHS), Nov 2020, Lille, France. ⟨hal-03351749⟩



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