A Delaunay triangulation based approach for cleaning rough sketches

Parakkat, Amal Dev; Pundarikaksha, Uday Bondi; Muthuganapathy, Ramanathan

doi:10.1016/j.cag.2018.05.011

Cited by 32 publications

(20 citation statements)

References 18 publications

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“…Numerous later works have been proposed following this same basic cluster-and-replace framework for vector graphics [Liu et al 2018[Liu et al , 2015Ogawa et al 2016;Orbay and Kara 2011;Shesh and Chen 2008]. Another line of work simultaneously vectorizes and simplifies a raster image of a sketch [Bessmeltsev and Solomon 2019;Donati et al 2019;Favreau et al 2016;Kim et al 2018;Noris et al 2013;Parakkat et al 2018]. This is a more challenging problem, as parametric data is unavailable for the input curves.…”

Section: Related Workmentioning

confidence: 99%

“…Such algorithms have been explored in the past [Barla et al 2005;Orbay and Kara 2011]. In the last five years, there has been a flurry of work [Bessmeltsev and Solomon 2019;Favreau et al 2016;Liu et al 2018Liu et al , 2015Noris et al 2013;Parakkat et al 2018;Simo-Serra et al 2018a,b]. These works differ substantially in the assumptions they make on the input and output sketches.…”

Section: Introductionmentioning

confidence: 99%

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A benchmark for rough sketch cleanup

2020

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A benchmark for rough sketch cleanup

2020

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“…This global optimization process presents a tradeoff between fidelity and simplicity. The approach in [11] uses the Delauney triangulation of a point set to identify the regions of a graph and to extract its skeleton for further processing, which shares similarities with [12,13].…”

Section: Related Workmentioning

confidence: 99%

Sketch simplification guided by complex agglomeration

Liu

et al. 2019

Sci. China Inf. Sci.

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We propose a novel method for vector sketch simplification based on the simplification of the geometric structure that is extracted from the input vector graph, which can be referred to as a base complex. Unlike the sets of strokes, which are treated in the existing approaches, a base complex is considered to be a collection of various geometric primitives. Guided by the shape similarity metrics that are defined for the base complex, an agglomeration procedure is proposed to simplify the base complex by iteratively merging a pair of geometric primitives that exhibit the minimum cost into a new one. This simplified base complex is finally converted into a simplified vector graph. Our algorithm is computationally efficient and is able to retain a large amount of useful shape information from the original vector graph, thereby achieving a tradeoff between efficiency and geometric fidelity. Furthermore, the level of simplification of the input vector graph can be easily controlled using a single threshold in our method. We make comparisons with some existing methods using the datasets that have been provided in the corresponding studies as well as using different styles of sketches drawn by artists. Thus, our experiments demonstrate the computational efficiency of our method and its capability for producing the desirable results.

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“…The rectangular criterion (e.g., see [22]) is used to evaluate the accuracy of the approximation, and supposes that the original points would exist in a rectangular area like the rectangular edge. A similar concept can also be seen in [23]. Using a similar criterion, it attempts to group the rough strokes drawn by a pen, which correspond to the contour points.…”

Section: Representationmentioning

confidence: 99%

Sampling Shape Contours Using Optimization over a Geometric Graph

Ose

Iwata

Saito

2019

IEICE Trans. Inf. & Syst.

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Consider selecting points on a contour in the x-y plane. In shape analysis, this is frequently referred to as contour sampling. It is important to select the points such that they effectively represent the shape of the contour. Generally, the stroke order and number of strokes are informative for that purpose. Several effective methods exist for sampling contours drawn with a certain stroke order and number of strokes, such as the English alphabet or Arabic figures. However, many contours entail an uncertain stroke order and number of strokes, such as pictures of symbols, and little research has focused on methods for sampling such contours. This is because selecting the points in this case typically requires a large computational cost to check all the possible choices. In this paper, we present a sampling method that is useful regardless of whether the contours are drawn with a certain stroke order and number of strokes or not. Our sampling method thereby expands the application possibilities of contour processing. We formulate contour sampling as a discrete optimization problem that can be solved using a type of direct search. Based on a geometric graph whose vertices are the points and whose edges form rectangles, we construct an effective objective function for the problem. Using different shape datasets, we demonstrate that our sampling method is effective with respect to shape representation and retrieval.

show abstract

A Delaunay triangulation based approach for cleaning rough sketches

Cited by 32 publications

References 18 publications

A benchmark for rough sketch cleanup

A benchmark for rough sketch cleanup

Sketch simplification guided by complex agglomeration

Sampling Shape Contours Using Optimization over a Geometric Graph

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