Название: Distant Viewing: Computational Exploration of Digital ImagesАвтор: Taylor Arnold, Lauren Tilton
Издательство: The MIT Press
Год: 2023
Страниц: 287
Язык: английский
Формат: pdf (true)
Размер: 39.9 MB
A new theory and methodology for the application of computer vision methods to the computational analysis of collected, digitized visual materials, called “distant viewing.”
Distant Viewing: Computational Exploration of Digital Images presents a new theory and methodology for the computational analysis of digital images, offering a lively, constructive critique of computer vision that you can actually use. What does it mean to say that computer vision “understands” visual inputs? Annotations never capture a whole image. The way digital images convey information requires what researchers Taylor Arnold and Lauren Tilton call “distant viewing”—a play on the well-known term “distant reading” from computational literary analysis.
Excitement has recently increased about the use of computer vision algorithms— computational methods that try to replicate elements of the human visual system— to assist in the study of visual collections. Algorithms applied to digital images through software can process large amounts of data in significantly less time than would be required to perform a similar task manually. Computers can aggregate patterns and surface connections that may be difficult to detect otherwise. These insights can lead to new ways of seeing and exploring visual data.
Recognizing computer vision’s limitations, Arnold and Tilton’s spirited examination makes the technical exciting by applying distant viewing to the sitcoms Bewitched and I Dream of Jeannie, movie posters and other popular forms of advertising, and Dorothea Lange’s photography. In the tradition of visual culture studies and computer vision, Distant Viewing’s interdisciplinary perspective encompasses film and media studies, visual semiotics, and the sciencesto create a playful, accessible guide for an international audience working in digital humanities, Data Science, media studies, and visual culture studies.
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