Crawl through Neighbors: A Simple Curve Reconstruction Algorithm

Given a planar point set sampled from an object boundary, the process of approximating the original shape is called curve reconstruction. In this paper, a novel non‐parametric curve reconstruction algorithm based on Delaunay triangulation has been proposed and it has been theoretically proved that t...

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Vydané v:Computer graphics forum Ročník 35; číslo 5; s. 177 - 186
Hlavní autori: Parakkat, Amal Dev, Muthuganapathy, Ramanathan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.08.2016
Predmet:
Algorithms
Analysis
Approximation
Boundaries
Categories and Subject Descriptors (according to ACM CCS)
Computer graphics
Corners
Delaunay triangulation
I.3.3 [Computer Graphics]: Picture/Image Generation-Line and curve generation
Image processing systems
Reconstruction
Sketches
Skips
Studies
Topological manifolds
ISSN:0167-7055, 1467-8659
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Popis
Shrnutí:Given a planar point set sampled from an object boundary, the process of approximating the original shape is called curve reconstruction. In this paper, a novel non‐parametric curve reconstruction algorithm based on Delaunay triangulation has been proposed and it has been theoretically proved that the proposed method reconstructs the original curve under ε‐sampling. Starting from an initial Delaunay seed edge, the algorithm proceeds by finding an appropriate neighbouring point and adding an edge between them. Experimental results show that the proposed algorithm is capable of reconstructing curves with different features like sharp corners, outliers, multiple objects, objects with holes, etc. The proposed method also works for open curves. Based on a study by a few users, the paper also discusses an application of the proposed algorithm for reconstructing hand drawn skip stroke sketches, which will be useful in various sketch based interfaces.
Bibliografia:ark:/67375/WNG-5BPZKDTS-X
ArticleID:CGF12974
istex:59C4446F706F9F8AD25952C2E12B1E2D7569DF60
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12974