Algorithmic Perception of Vertices in Sketched Drawings of Polyhedral Shapes
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comunitat-uji-handle2:10234/43662
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Title
Algorithmic Perception of Vertices in Sketched Drawings of Polyhedral ShapesDate
2019-09Publisher
Association for Computing Machinery (ACM)ISSN
1544-3558; 1544-3965Bibliographic citation
COMPANY, Pedro, et al. Algorithmic Perception of Vertices in Sketched Drawings of Polyhedral Shapes. ACM Transactions on Applied Perception, 2019, vol. 16, no 3, p. 18Type
info:eu-repo/semantics/articlePublisher version
https://dl.acm.org/ft_gateway.cfm?id=3345507&ftid=2081879&dwn=1&CFID=102782805&C ...Version
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Abstract
In this article, visual perception principles were used to build an artificial perception model aimed at developing an algorithm for detecting junctions in line drawings of polyhedral objects that are vectorized from ... [+]
In this article, visual perception principles were used to build an artificial perception model aimed at developing an algorithm for detecting junctions in line drawings of polyhedral objects that are vectorized from hand-drawn sketches. The detection is performed in two dimensions (2D), before any 3D model is available and minimal information about the shape depicted by the sketch is used. The goal of this approach is to not only detect junctions in careful sketches created by skilled engineers and designers but also detect junctions when skilled people draw casually to quickly convey rough ideas. Current approaches for extracting junctions from digital images are mostly incomplete, as they simply merge endpoints that are near each other, thus ignoring the fact that different vertices may be represented by different (but close) junctions and that the endpoints of lines that depict edges that share a common vertex may not necessarily be close to each other, particularly in quickly sketched drawings. We describe and validate a new algorithm that uses these perceptual findings to merge tips of line segments into 2D junctions that are assumed to depict 3D vertices. [-]
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ACM Transactions on Applied Perception, 2019, vol. 16, no 3Rights
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