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dc.contributor.authorGuțan, Olga
dc.contributor.authorHegde, Shreya
dc.contributor.authorJimenez Berumen, Erick
dc.contributor.authorBessmeltsev, Mikhail
dc.contributor.authorChien, Edward
dc.date.accessioned2023-07-19T15:40:20Z
dc.date.availableMONTHS_WITHHELD:12fr
dc.date.available2023-07-19T15:40:20Z
dc.date.issued2023-07-19
dc.identifier.urihttp://hdl.handle.net/1866/28422
dc.publisherWileyfr
dc.subjectComputing methodologiesfr
dc.subjectParametric curve and surface modelsfr
dc.subjectShape analysisfr
dc.titleSingularity-free frame fields for line drawing vectorizationfr
dc.typeArticlefr
dc.contributor.affiliationUniversité de Montréal. Faculté des arts et des sciences. Département d'informatique et de recherche opérationnellefr
dc.identifier.doi10.1111/cgf.14901
dcterms.abstractState-of-the-art methods for line drawing vectorization rely on generated frame fields for robust direction disambiguation, with each of the two axes aligning to different intersecting curve tangents around junctions. However, a common source of topological error for such methods are frame field singularities. To remedy this, we introduce the first frame field optimization framework guaranteed to produce singularity-free fields aligned to a line drawing. We first perform a convex solve for a roughly-aligned orthogonal frame field (cross field), and then comb away its internal singularities with an optimal transport–based matching. The resulting topology of the field is strictly maintained with the machinery of discrete trivial connections in a final, non-convex optimization that allows non-orthogonality of the field, improving smoothness and tangent alignment. Our frame fields can serve as a drop-in replacement for frame field optimizations used in previous work, improving the quality of the final vectorizations.fr
dcterms.isPartOfurn:ISSN:1467-8659fr
dcterms.isPartOfurn:ISSN:0167-7055fr
dcterms.languageengfr
UdeM.ReferenceFournieParDeposantSingularity-Free Frame Fields for Line Drawing Vectorization par Olga Guțan, Shreya Hegde, Erick Jimenez Berumen, Mikhail Bessmeltsev, Edward Chien. Accepté à Symposium on Geometry Processing 2023fr
UdeM.VersionRioxxVersion acceptée / Accepted Manuscriptfr
oaire.citationTitleComputer graphics forumfr
oaire.citationVolume42fr
oaire.citationIssue5fr


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