A combined multi-scale/irregular algorithm for the vectorization of noisy digital contours
Identifieur interne : 000081 ( PascalFrancis/Corpus ); précédent : 000080; suivant : 000082A combined multi-scale/irregular algorithm for the vectorization of noisy digital contours
Auteurs : Antoine Vacavant ; Tristan Roussillon ; Bertrand Kerautret ; Jacques-Olivier LachaudSource :
- Computer vision and image understanding : (Print) [ 1077-3142 ] ; 2013.
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English descriptors
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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.
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NO : | PASCAL 13-0182220 INIST |
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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 |
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Pascal:13-0182220Le document en format XML
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<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>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Error estimation</s0>
<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>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Irregular Algorithm</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Algoritmo irregular</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fN21><s1>161</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
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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>
<LO>INIST-15463A.354000502487160120</LO>
<ID>13-0182220</ID>
</server>
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