Reconstructions of Noisy Digital Contours with Maximal Primitives Based on Multi-scale/Irregular Geometric Representation and Generalized Linear Programming
Abstract:The reconstruction of noisy digital shapes is a complex question and a lot of contributions have been proposed to address this problem, including blurred segment decomposition or adaptive tangential covering for instance. In this article, we propose a novel approach combining multi-scale and irregular isothetic representations of the input contour, as an extension of a previous work [Vacavant et al., A Combined Multi-Scale/Irregular Algorithm for the Vectorization of Noisy Digital Contours, CVIU 2013]. Our new… Show more
“…We have proposed a novel framework to vectorize possibly noisy digital contours by line segments or circular arcs, by combining our previous work devoted to reconstruct maximal primitives [ 22 ] and the tangential cover algorithm minDSS [ 7 ]. By means of the experiments we have exposed, that this method achieves faithful vectorization according to the underlying object contour.…”
Section: Discussionmentioning
confidence: 99%
“…Figure 4 depicts the results obtained for two noisy digital contours, using maximal segments or arcs. More details can be found in [ 22 ]. Fig.…”
Section: Reconstructions Into Maximal Primitivesmentioning
confidence: 99%
“…Following this literature of digital geometrical-based approaches, in this article, we propose a complete pipeline based on our previous contribution [ 22 ] in order to vectorize noisy digital contours into line segments or circular arcs. This related work aimed at representing such contours with maximal primitives, by processing 1-D (one-dimensional) intervals obtained by a multi-scale and irregular approach, summarized in Fig.…”
Section: Introductionmentioning
confidence: 99%
“…1. Global work-flow of our vectorization approach in relation to previous work [ 22 ] (in red) and new reconstruction, highlighted on the right (green). (Color figure online) …”
Section: Introductionmentioning
confidence: 99%
“…The article is organized as follows. Section 2 relates synthetically on our previous contribution devoted to represent noisy digital contours into maximal primitives (lines or arcs) [ 22 ]. Then, Sect.…”
In this paper, we propose an original manner to employ a tangential cover algorithm - minDSS - in order to vectorize noisy digital contours. To do so, we exploit the representation of graphical objects by maximal primitives we have introduced in previous work. By calculating multi-scale and irregular isothetic representations of the contour, we obtained 1-D (one-dimensional) intervals, and achieved afterwards a decomposition into maximal line segments or circular arcs. By adapting minDSS to this sparse and irregular data of 1-D intervals supporting the maximal primitives, we are now able to reconstruct the input noisy objects into cyclic contours made of lines or arcs with a minimal number of primitives. We explain our novel complete pipeline in this work, and present its experimental evaluation by considering both synthetic and real image data.
“…We have proposed a novel framework to vectorize possibly noisy digital contours by line segments or circular arcs, by combining our previous work devoted to reconstruct maximal primitives [ 22 ] and the tangential cover algorithm minDSS [ 7 ]. By means of the experiments we have exposed, that this method achieves faithful vectorization according to the underlying object contour.…”
Section: Discussionmentioning
confidence: 99%
“…Figure 4 depicts the results obtained for two noisy digital contours, using maximal segments or arcs. More details can be found in [ 22 ]. Fig.…”
Section: Reconstructions Into Maximal Primitivesmentioning
confidence: 99%
“…Following this literature of digital geometrical-based approaches, in this article, we propose a complete pipeline based on our previous contribution [ 22 ] in order to vectorize noisy digital contours into line segments or circular arcs. This related work aimed at representing such contours with maximal primitives, by processing 1-D (one-dimensional) intervals obtained by a multi-scale and irregular approach, summarized in Fig.…”
Section: Introductionmentioning
confidence: 99%
“…1. Global work-flow of our vectorization approach in relation to previous work [ 22 ] (in red) and new reconstruction, highlighted on the right (green). (Color figure online) …”
Section: Introductionmentioning
confidence: 99%
“…The article is organized as follows. Section 2 relates synthetically on our previous contribution devoted to represent noisy digital contours into maximal primitives (lines or arcs) [ 22 ]. Then, Sect.…”
In this paper, we propose an original manner to employ a tangential cover algorithm - minDSS - in order to vectorize noisy digital contours. To do so, we exploit the representation of graphical objects by maximal primitives we have introduced in previous work. By calculating multi-scale and irregular isothetic representations of the contour, we obtained 1-D (one-dimensional) intervals, and achieved afterwards a decomposition into maximal line segments or circular arcs. By adapting minDSS to this sparse and irregular data of 1-D intervals supporting the maximal primitives, we are now able to reconstruct the input noisy objects into cyclic contours made of lines or arcs with a minimal number of primitives. We explain our novel complete pipeline in this work, and present its experimental evaluation by considering both synthetic and real image data.
In this paper, we propose an original manner to employ a tangential cover algorithm -minDSS -in order to geometrically reconstruct noisy digital contours. To do so, we exploit the representation of graphical objects by maximal primitives we have introduced in previous works. By calculating multi-scale and irregular isothetic representations of the contour, we obtained 1-D (one-dimensional) intervals, and achieved afterwards a decomposition into maximal line segments or circular arcs. By adapting minDSS to this sparse and irregular data of 1-D intervals supporting the maximal primitives, we are now able to reconstruct the input noisy objects into cyclic contours made of lines or arcs with a minimal number of primitives. In this work, we explain our novel complete pipeline, and present its experimental evaluation by considering both synthetic and real image data. We also show that this is a robust approach, with respect to selected references from state-of-the-art, and by considering a multi-scale noise evaluation process.
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