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Abstract art by shape classification

Song, Yi-Zhe, Pickup, David, Li, Chuan, Rosin, Paul L. and Hall, P. 2013. Abstract art by shape classification. IEEE Transactions on Visualization and Computer Graphics 19 (8) , pp. 1252-1263. 10.1109/TVCG.2013.13

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Abstract

his paper shows that classifying shapes is a tool useful in nonphotorealistic rendering (NPR) from photographs. Our classifier inputs regions from an image segmentation hierarchy and outputs the "best” fitting simple shape such as a circle, square, or triangle. Other approaches to NPR have recognized the benefits of segmentation, but none have classified the shape of segments. By doing so, we can create artwork of a more abstract nature, emulating the style of modern artists such as Matisse and other artists who favored shape simplification in their artwork. The classifier chooses the shape that "best” represents the region. Since the classifier is trained by a user, the "best shape” has a subjective quality that can over-ride measurements such as minimum error and more importantly captures user preferences. Once trained, the system is fully automatic, although simple user interaction is also possible to allow for differences in individual tastes. A gallery of results shows how this classifier contributes to NPR from images by producing abstract artwork. INDEX TERMS

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: IEEE
ISSN: 1077-2626
Date of First Compliant Deposit: 30 March 2016
Last Modified: 27 Jun 2019 14:02
URI: http://orca.cf.ac.uk/id/eprint/66367

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Cited 3 times in Google Scholar. View in Google Scholar

Cited 12 times in Scopus. View in Scopus. Powered By Scopus® Data

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