InforLorV4, PascalFrancis, Corpus, bibRecord, 000081

A combined multi-scale/irregular algorithm for the vectorization of noisy digital contours

Identifieur interne : 000081 ( PascalFrancis/Corpus ); précédent : 000080; suivant : 000082

A combined multi-scale/irregular algorithm for the vectorization of noisy digital contours

Auteurs : Antoine Vacavant ; Tristan Roussillon ; Bertrand Kerautret ; Jacques-Olivier Lachaud

Source :

Computer vision and image understanding : (Print) [ 1077-3142 ] ; 2013.

RBID : Pascal:13-0182220

Descripteurs français

Pascal (Inist)
- Vision ordinateur, Détection contour, Topologie, Géométrie algorithmique, Image numérique, Grille, Endommagement, Réalité terrain, Vectorisation, Segmentation, Rapport signal bruit, Recouvrement ensemble, Ensemble ordonné, Elagage, Estimation erreur, ., Algorithme irrégulier.

English descriptors

KwdEn :
- Computational geometry, Computer vision, Damaging, Digital image, Edge detection, Error estimation, Grid, Ground truth, Irregular Algorithm, Ordered set, Pruning(tree), Segmentation, Set covering, Signal to noise ratio, Topology, Vectorization.

Abstract

This paper proposes and evaluates a new method for reconstructing a polygonal representation from arbitrary digital contours that are possibly damaged or coming from the segmentation of noisy data. The method consists in two stages. In the first stage, a multi-scale analysis of the contour is conducted so as to identify noisy or damaged parts of the contour as well as the intensity of the perturbation. All the identified scales are then merged so that the input data is covered by a set of pixels whose size is increased according to the local intensity of noise. The second stage consists in transforming this set of resized pixels into an irregular isothetic object composed of an ordered set of rectangular and axis-aligned cells. Its topology is stored as a Reeb graph, which allows an easy pruning of its unnecessary spurious edges. Every remaining connected part has the topology of a circle and a polygonal representation is independently computed for each of them. Four different geometrical algorithms, including a new one, are reviewed for the latter task. These vectorization algorithms are experimentally evaluated and the whole method is also compared to previous works on both synthetic and true digital images. For fair comparisons, when possible, several error measures between the reconstruction and the ground truth are given for the different techniques.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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Format Inist (serveur)

NO :	PASCAL 13-0182220 INIST
ET :	A combined multi-scale/irregular algorithm for the vectorization of noisy digital contours
AU :	VACAVANT (Antoine); ROUSSILLON (Tristan); KERAUTRET (Bertrand); LACHAUD (Jacques-Olivier); DEBLED-RENNESSON (Isabelle); DOMENJOUD (Eric); KERAUTRET (Bertrand); EVEN (Philippe)
AF :	Clermont Université, Université d'Auvergne, ISIT, BP10448/63000 Clermont-Ferrand/France (1 aut.); CNRS, UMR6284, BP10448/63000 Clermont-Ferrand/France (1 aut.); Université de Lyon, CNRS/Lyon/France (2 aut.); Université Lyon 2, LIRIS, UMR5205 CNRS/69676, Lyon/France (2 aut.); Université de Nancy, LORIA, UMR7503 CNRS/54506, Nancy/France (3 aut.); Université de Savoie, LAMA, UMR5127 CNRS/73376, Chambéry/France (3 aut., 4 aut.); LORIA (Lorraine Research Laboratory in Computer Science and its Applications), UMR 7503, Lorraine University/Nancy/France (1 aut., 2 aut., 3 aut., 4 aut.)
DT :	Publication en série; Niveau analytique
SO :	Computer vision and image understanding : (Print); ISSN 1077-3142; Coden CVIUF4; Pays-Bas; Da. 2013; Vol. 117; No. 4; Pp. 438-450; Bibl. 38 ref.
LA :	Anglais
EA :	This paper proposes and evaluates a new method for reconstructing a polygonal representation from arbitrary digital contours that are possibly damaged or coming from the segmentation of noisy data. The method consists in two stages. In the first stage, a multi-scale analysis of the contour is conducted so as to identify noisy or damaged parts of the contour as well as the intensity of the perturbation. All the identified scales are then merged so that the input data is covered by a set of pixels whose size is increased according to the local intensity of noise. The second stage consists in transforming this set of resized pixels into an irregular isothetic object composed of an ordered set of rectangular and axis-aligned cells. Its topology is stored as a Reeb graph, which allows an easy pruning of its unnecessary spurious edges. Every remaining connected part has the topology of a circle and a polygonal representation is independently computed for each of them. Four different geometrical algorithms, including a new one, are reviewed for the latter task. These vectorization algorithms are experimentally evaluated and the whole method is also compared to previous works on both synthetic and true digital images. For fair comparisons, when possible, several error measures between the reconstruction and the ground truth are given for the different techniques.
CC :	001D02A05; 001D02C03
FD :	Vision ordinateur; Détection contour; Topologie; Géométrie algorithmique; Image numérique; Grille; Endommagement; Réalité terrain; Vectorisation; Segmentation; Rapport signal bruit; Recouvrement ensemble; Ensemble ordonné; Elagage; Estimation erreur; .; Algorithme irrégulier
ED :	Computer vision; Edge detection; Topology; Computational geometry; Digital image; Grid; Damaging; Ground truth; Vectorization; Segmentation; Signal to noise ratio; Set covering; Ordered set; Pruning(tree); Error estimation; Irregular Algorithm
SD :	Visión ordenador; Detección contorno; Topología; Geometría computacional; Imagen numérica; Rejilla; Deterioración; Realidad terreno; Vectorización; Segmentación; Relación señal ruido; Cubierta conjunto; Conjunto ordenado; Poda; Estimación error; Algoritmo irregular
LO :	INIST-15463A.354000502487160120
ID :	13-0182220

Links to Exploration step

Pascal:13-0182220

Le document en format XML

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<sZ>2 aut.</sZ>
</fA14>
<fA14 i1="05"><s1>Université de Nancy, LORIA, UMR7503 CNRS</s1>
<s2>54506, Nancy</s2>
<s3>FRA</s3>
<sZ>3 aut.</sZ>
</fA14>
<fA14 i1="06"><s1>Université de Savoie, LAMA, UMR5127 CNRS</s1>
<s2>73376, Chambéry</s2>
<s3>FRA</s3>
<sZ>3 aut.</sZ>
<sZ>4 aut.</sZ>
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<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
<sZ>4 aut.</sZ>
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</fC01>
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<s5>01</s5>
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<s5>01</s5>
</fC03>
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<s5>06</s5>
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<s5>06</s5>
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<s5>07</s5>
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<s5>07</s5>
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<s5>07</s5>
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<s5>08</s5>
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<s5>08</s5>
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<fC03 i1="04" i2="X" l="SPA"><s0>Geometría computacional</s0>
<s5>08</s5>
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<fC03 i1="05" i2="X" l="FRE"><s0>Image numérique</s0>
<s5>09</s5>
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<s5>09</s5>
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<s5>10</s5>
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<fC03 i1="06" i2="X" l="ENG"><s0>Grid</s0>
<s5>10</s5>
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<s5>10</s5>
</fC03>
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<s5>18</s5>
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<fC03 i1="07" i2="X" l="ENG"><s0>Damaging</s0>
<s5>18</s5>
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<s5>18</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Réalité terrain</s0>
<s5>19</s5>
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<fC03 i1="08" i2="X" l="ENG"><s0>Ground truth</s0>
<s5>19</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Realidad terreno</s0>
<s5>19</s5>
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<fC03 i1="09" i2="X" l="FRE"><s0>Vectorisation</s0>
<s5>23</s5>
</fC03>
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<s5>23</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Vectorización</s0>
<s5>23</s5>
</fC03>
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<s5>24</s5>
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<s5>24</s5>
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<s5>24</s5>
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<s5>25</s5>
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<s5>25</s5>
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<s5>25</s5>
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<s5>26</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Set covering</s0>
<s5>26</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Cubierta conjunto</s0>
<s5>26</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Ensemble ordonné</s0>
<s5>27</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Ordered set</s0>
<s5>27</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Conjunto ordenado</s0>
<s5>27</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Elagage</s0>
<s5>28</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Pruning(tree)</s0>
<s5>28</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Poda</s0>
<s5>28</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Estimation erreur</s0>
<s5>29</s5>
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<s5>29</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Estimación error</s0>
<s5>29</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>.</s0>
<s4>INC</s4>
<s5>82</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Algorithme irrégulier</s0>
<s4>CD</s4>
<s5>96</s5>
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<s4>CD</s4>
<s5>96</s5>
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<fC03 i1="17" i2="X" l="SPA"><s0>Algoritmo irregular</s0>
<s4>CD</s4>
<s5>96</s5>
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<fN21><s1>161</s1>
</fN21>
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<fN82><s1>OTO</s1>
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<server><NO>PASCAL 13-0182220 INIST</NO>
<ET>A combined multi-scale/irregular algorithm for the vectorization of noisy digital contours</ET>
<AU>VACAVANT (Antoine); ROUSSILLON (Tristan); KERAUTRET (Bertrand); LACHAUD (Jacques-Olivier); DEBLED-RENNESSON (Isabelle); DOMENJOUD (Eric); KERAUTRET (Bertrand); EVEN (Philippe)</AU>
<AF>Clermont Université, Université d'Auvergne, ISIT, BP10448/63000 Clermont-Ferrand/France (1 aut.); CNRS, UMR6284, BP10448/63000 Clermont-Ferrand/France (1 aut.); Université de Lyon, CNRS/Lyon/France (2 aut.); Université Lyon 2, LIRIS, UMR5205 CNRS/69676, Lyon/France (2 aut.); Université de Nancy, LORIA, UMR7503 CNRS/54506, Nancy/France (3 aut.); Université de Savoie, LAMA, UMR5127 CNRS/73376, Chambéry/France (3 aut., 4 aut.); LORIA (Lorraine Research Laboratory in Computer Science and its Applications), UMR 7503, Lorraine University/Nancy/France (1 aut., 2 aut., 3 aut., 4 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Computer vision and image understanding : (Print); ISSN 1077-3142; Coden CVIUF4; Pays-Bas; Da. 2013; Vol. 117; No. 4; Pp. 438-450; Bibl. 38 ref.</SO>
<LA>Anglais</LA>
<EA>This paper proposes and evaluates a new method for reconstructing a polygonal representation from arbitrary digital contours that are possibly damaged or coming from the segmentation of noisy data. The method consists in two stages. In the first stage, a multi-scale analysis of the contour is conducted so as to identify noisy or damaged parts of the contour as well as the intensity of the perturbation. All the identified scales are then merged so that the input data is covered by a set of pixels whose size is increased according to the local intensity of noise. The second stage consists in transforming this set of resized pixels into an irregular isothetic object composed of an ordered set of rectangular and axis-aligned cells. Its topology is stored as a Reeb graph, which allows an easy pruning of its unnecessary spurious edges. Every remaining connected part has the topology of a circle and a polygonal representation is independently computed for each of them. Four different geometrical algorithms, including a new one, are reviewed for the latter task. These vectorization algorithms are experimentally evaluated and the whole method is also compared to previous works on both synthetic and true digital images. For fair comparisons, when possible, several error measures between the reconstruction and the ground truth are given for the different techniques.</EA>
<CC>001D02A05; 001D02C03</CC>
<FD>Vision ordinateur; Détection contour; Topologie; Géométrie algorithmique; Image numérique; Grille; Endommagement; Réalité terrain; Vectorisation; Segmentation; Rapport signal bruit; Recouvrement ensemble; Ensemble ordonné; Elagage; Estimation erreur; .; Algorithme irrégulier</FD>
<ED>Computer vision; Edge detection; Topology; Computational geometry; Digital image; Grid; Damaging; Ground truth; Vectorization; Segmentation; Signal to noise ratio; Set covering; Ordered set; Pruning(tree); Error estimation; Irregular Algorithm</ED>
<SD>Visión ordenador; Detección contorno; Topología; Geometría computacional; Imagen numérica; Rejilla; Deterioración; Realidad terreno; Vectorización; Segmentación; Relación señal ruido; Cubierta conjunto; Conjunto ordenado; Poda; Estimación error; Algoritmo irregular</SD>
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A combined multi-scale/irregular algorithm for the vectorization of noisy digital contours

A combined multi-scale/irregular algorithm for the vectorization of noisy digital contours

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