Image meshing via hierarchical optimization

Abstract Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithful...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xie, Hao [verfasserIn] Tong, Ruo-feng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Image meshing Hierarchical optimization Convexification

Übergeordnetes Werk:	Enthalten in: Journal of Zhejiang University - Hangzhou : Zhejiang Univ. Press, 2010, 17(2016), 1 vom: Jan., Seite 32-40
Übergeordnetes Werk:	volume:17 ; year:2016 ; number:1 ; month:01 ; pages:32-40

Links:	Volltext

DOI / URN:	10.1631/FITEE.1500171

Katalog-ID:	SPR022008004

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allfields	10.1631/FITEE.1500171 doi (DE-627)SPR022008004 (SPR)FITEE.1500171-e DE-627 ger DE-627 rakwb eng 004 ASE Xie, Hao verfasserin aut Image meshing via hierarchical optimization 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images. Image meshing (dpeaa)DE-He213 Hierarchical optimization (dpeaa)DE-He213 Convexification (dpeaa)DE-He213 Tong, Ruo-feng verfasserin aut Enthalten in Journal of Zhejiang University Hangzhou : Zhejiang Univ. Press, 2010 17(2016), 1 vom: Jan., Seite 32-40 (DE-627)618789693 (DE-600)2537865-X 1869-196X nnns volume:17 year:2016 number:1 month:01 pages:32-40 https://dx.doi.org/10.1631/FITEE.1500171 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 AR 17 2016 1 01 32-40
spelling	10.1631/FITEE.1500171 doi (DE-627)SPR022008004 (SPR)FITEE.1500171-e DE-627 ger DE-627 rakwb eng 004 ASE Xie, Hao verfasserin aut Image meshing via hierarchical optimization 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images. Image meshing (dpeaa)DE-He213 Hierarchical optimization (dpeaa)DE-He213 Convexification (dpeaa)DE-He213 Tong, Ruo-feng verfasserin aut Enthalten in Journal of Zhejiang University Hangzhou : Zhejiang Univ. Press, 2010 17(2016), 1 vom: Jan., Seite 32-40 (DE-627)618789693 (DE-600)2537865-X 1869-196X nnns volume:17 year:2016 number:1 month:01 pages:32-40 https://dx.doi.org/10.1631/FITEE.1500171 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 AR 17 2016 1 01 32-40
allfields_unstemmed	10.1631/FITEE.1500171 doi (DE-627)SPR022008004 (SPR)FITEE.1500171-e DE-627 ger DE-627 rakwb eng 004 ASE Xie, Hao verfasserin aut Image meshing via hierarchical optimization 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images. Image meshing (dpeaa)DE-He213 Hierarchical optimization (dpeaa)DE-He213 Convexification (dpeaa)DE-He213 Tong, Ruo-feng verfasserin aut Enthalten in Journal of Zhejiang University Hangzhou : Zhejiang Univ. Press, 2010 17(2016), 1 vom: Jan., Seite 32-40 (DE-627)618789693 (DE-600)2537865-X 1869-196X nnns volume:17 year:2016 number:1 month:01 pages:32-40 https://dx.doi.org/10.1631/FITEE.1500171 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 AR 17 2016 1 01 32-40
allfieldsGer	10.1631/FITEE.1500171 doi (DE-627)SPR022008004 (SPR)FITEE.1500171-e DE-627 ger DE-627 rakwb eng 004 ASE Xie, Hao verfasserin aut Image meshing via hierarchical optimization 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images. Image meshing (dpeaa)DE-He213 Hierarchical optimization (dpeaa)DE-He213 Convexification (dpeaa)DE-He213 Tong, Ruo-feng verfasserin aut Enthalten in Journal of Zhejiang University Hangzhou : Zhejiang Univ. Press, 2010 17(2016), 1 vom: Jan., Seite 32-40 (DE-627)618789693 (DE-600)2537865-X 1869-196X nnns volume:17 year:2016 number:1 month:01 pages:32-40 https://dx.doi.org/10.1631/FITEE.1500171 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 AR 17 2016 1 01 32-40
allfieldsSound	10.1631/FITEE.1500171 doi (DE-627)SPR022008004 (SPR)FITEE.1500171-e DE-627 ger DE-627 rakwb eng 004 ASE Xie, Hao verfasserin aut Image meshing via hierarchical optimization 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images. Image meshing (dpeaa)DE-He213 Hierarchical optimization (dpeaa)DE-He213 Convexification (dpeaa)DE-He213 Tong, Ruo-feng verfasserin aut Enthalten in Journal of Zhejiang University Hangzhou : Zhejiang Univ. Press, 2010 17(2016), 1 vom: Jan., Seite 32-40 (DE-627)618789693 (DE-600)2537865-X 1869-196X nnns volume:17 year:2016 number:1 month:01 pages:32-40 https://dx.doi.org/10.1631/FITEE.1500171 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 AR 17 2016 1 01 32-40
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source	Enthalten in Journal of Zhejiang University 17(2016), 1 vom: Jan., Seite 32-40 volume:17 year:2016 number:1 month:01 pages:32-40
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topic_title	004 ASE Image meshing via hierarchical optimization Image meshing (dpeaa)DE-He213 Hierarchical optimization (dpeaa)DE-He213 Convexification (dpeaa)DE-He213
topic	ddc 004 misc Image meshing misc Hierarchical optimization misc Convexification
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title	Image meshing via hierarchical optimization
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title_sort	image meshing via hierarchical optimization
title_auth	Image meshing via hierarchical optimization
abstract	Abstract Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.
abstractGer	Abstract Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.
abstract_unstemmed	Abstract Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.
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title_short	Image meshing via hierarchical optimization
url	https://dx.doi.org/10.1631/FITEE.1500171
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up_date	2024-07-04T01:23:21.504Z
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Image meshing via hierarchical optimization

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