Peeling the longest: A simple generalized curve reconstruction algorithm

Parakkat, Amal Dev; Methirumangalath, Subhasree; Muthuganapathy, Ramanathan

doi:10.1016/j.cag.2018.05.015

Cited by 17 publications

(16 citation statements)

References 19 publications

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“…A few data sets were generated from the corresponding images using mesecina software [MGP07]. We compared our algorithm with other Delaunay/Voronoi‐based algorithms such as crust [ABE98], nearest neighbour crust [DK99], ec‐shape [MPM15], shape‐hull [PM15b] and the recent algorithms in [PM16] and [PMM18]. We also evaluate our method using simplification algorithms such as optimal transport‐based algorithm [dGCSAD11].…”

Section: Resultsmentioning

confidence: 99%

“… Outlier experiment : All the stages of outlier injection, dove shape reconstructed by the proposed algorithm, preserve fine details as compared to a simplified reconstruction by deGoes et al. [dGCSAD11] and the reconstruction with curve artefacts in [PM16] and [PMM18]. Outliers were generated using the software by deGoes et al.…”

Section: Resultsmentioning

confidence: 99%

“…So, we label all such EVVs as inner. All the remaining Voronoi vertices [dGCSAD11] and the reconstruction with curve artefacts in [PM16] and [PMM18]. Outliers were generated using the software by deGoes et al [dGCSAD11].…”

Section: Curve and Medial Axes Extractionmentioning

confidence: 99%

“…However, a few artefacts appear in the reconstruction for 40% outliers in all the algorithms. The reconstruction by Parakkat and Muthuganapathy [PM16] and Parakkat et al [PMM18] produces curve artefacts. Please note that, albeit the artefacts, dove shape has been well reconstructed by our method.…”

Section: Robustness To Outliersmentioning

confidence: 99%

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Incremental Labelling of Voronoi Vertices for Shape Reconstruction

Peethambaran

Parakkat

Tagliasacchi

et al. 2018

Computer Graphics Forum

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We present an incremental Voronoi vertex labelling algorithm for approximating contours, medial axes and dominant points (high curvature points) from 2D point sets. Though there exist many number of algorithms for reconstructing curves, medial axes or dominant points, a unified framework capable of approximating all the three in one place from points is missing in the literature. Our algorithm estimates the normals at each sample point through poles (farthest Voronoi vertices of a sample point) and uses the estimated normals and the corresponding tangents to determine the spatial locations (inner or outer) of the Voronoi vertices with respect to the original curve. The vertex classification helps to construct a piece‐wise linear approximation to the object boundary. We provide a theoretical analysis of the algorithm for points non‐uniformly (ε‐sampling) sampled from simple, closed, concave and smooth curves. The proposed framework has been thoroughly evaluated for its usefulness using various test data. Results indicate that even sparsely and non‐uniformly sampled curves with outliers or collection of curves are faithfully reconstructed by the proposed algorithm.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Curve and Medial Axes Extractionmentioning

confidence: 99%

Section: Robustness To Outliersmentioning

confidence: 99%

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Incremental Labelling of Voronoi Vertices for Shape Reconstruction

Peethambaran

Parakkat

Tagliasacchi

et al. 2018

Computer Graphics Forum

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“…However, when coloring a line drawing, the luminance histogram is not available. Curve reconstruction [18] can be used to produce a set of closed boundaries. However, it depends on the sampling rate and gaps are closed by straight lines due to Delaunay triangulation and the result is similar to [17].…”

Section: Related Workmentioning

confidence: 99%

Handling gaps for vector graphics coloring

Jiang

Seah

Liew³

2021

Vis Comput

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In computer-assisted 2D vector drawing systems, due to the precision of stroke representation, a stroke endpoint may not precisely connect to another stroke or endpoint during drawing so that the stroke endpoints become dangling. We call such dangling endpoints, gap points. With the presence of gaps on the region boundaries, regions formed by strokes frequently cannot be correctly colored using the standard 'flood fill' algorithm. In this paper, we propose a stroke-based technique to handle gaps in a vector drawing. The main contribution of our work is automatic computation of gap sizes of gap points without a predefined gap size and using a least energy method to handle gap points by estimating their relationship with surrounding strokes. Our approach consists of two major steps. Firstly, we cluster gap points in a drawing using Minimum Spanning Tree. Secondly, for each endpoint cluster, we use a least energy method together with predefined gap handling priority to estimate the connection of gap points. We demonstrate the effectiveness of our approach by applying it to line drawings with unresolved gap points.

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