Stable and Robust Vectorization: How to Make the Right Choices

Tombre, Karl; Ah-Soon, Christian; Dosch, Philippe; Masini, Gérald; Tabbone, Salvatore

doi:10.1007/3-540-40953-x_1

Cited by 54 publications

(51 citation statements)

References 32 publications

(17 reference statements)

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“…The second one analyses skeleton images in order to extract pixel graphs. It uses different methods, based on the connectivity analysis [54] [44], or the 3-connected pixel destruction [14] [27]. The Figure 2 (a) gives an example of an extracted pixel graph [14].…”

Section: Skeletonisation Based Methodsmentioning

confidence: 99%

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Local Structural Analysis: A Primer

Delalandre

Trupin

Ogier

2004

Graphics Recognition. Recent Advances and Perspectives

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The structural analysis is a processing step during which graphs are extracted from binary images. We can decompose the structural analysis into local and global approaches. The local approach decomposes the connected components, and the global approach groups them together. This paper deals especially with the local structural analysis. The local structural analysis is employed for different applications like symbol recognition, line drawing interpretation, and character recognition. We propose here a primer on the local structural analysis. First, we propose a general decomposition of the local structural analysis into four steps: object graph extraction, mathematical approximation, high-level object construction, and object graph correction. Then, we present some considerations on the method comparison and combination.

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Section: Skeletonisation Based Methodsmentioning

confidence: 99%

“…This comparison study is only based on a set of significant experiments performed in our laboratories. The skeletonisation based methods are invariant [54], but they only permit the linear shape analysis [45], and are noise sensitive (especially for the junction zone analysis [54]). …”

Section: Methods Comparisonmentioning

confidence: 99%

Local Structural Analysis: A Primer

Delalandre

Trupin

Ogier

2004

Graphics Recognition. Recent Advances and Perspectives

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“…In off-line interpretation algorithms, line strokes are identified using vectorization techniques whose scope is to locate the line strokes and represent these strokes as vectors which can be interpreted by computer-aided design (CAD) tools [Tombre et al 2000]. Vectorisation algorithms have been used in applications involving engi- Figure 2: A sketch with shading (a) is not bimodal as can be evidenced from the shape of its histogram (c).…”

Section: Related Workmentioning

confidence: 99%

A circle-based vectorization algorithm for drawings with shadows

Bonnici

Camilleri

2013

Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling

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This work is funded by the University of Malta, under the research grant SCERP02-03.Vectorization algorithms described in the literature assume that the drawings being vectorized are either binary images or have a clear white background. Sketches of artistic objects however also contain shadows which help the artist to portray intent, particularly in potentially ambiguous sketches. Such sketches are difficult to binarise since the shading strokes make these sketches non bimodal. For this reason, we describe a circle-based vectorization algorithm that uses signatures obtained from sample points on the line strokes to identify and vectorize the line strokes in the sketch. We show that the proposed algorithm performs as well as other vectorization techniques described in the literature, despite the shadows present in the sketch.peer-reviewe

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“…1C) using the new Rayburst sampling algorithm described below. We utilize a topology and geometry-preserving skeletonization method for volumetric objects (see Nikolaidis and Pitas, 2001, for review), with a custom modified iterative thinning algorithm (Lee et al, 1994;Borgefors et al, 1999;Tombre et al, 2000;Rodriguez et al, 2003a) to yield robust skeletons in an efficient manner. This is followed by loop removal, pruning to remove small barbs due to surface irregularities, tree smoothing and branch point repositioning (Tombre et al, 2000).…”

Section: Extraction Of Tree Topology Using Neuronstudiomentioning

confidence: 99%