Hybrid cost aggregation for dense stereo matching

Abstract Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be computed at each pixel for all disparities within the search range so that it can be used to evaluate pixel-to-pixel correspondence. Cost...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yao, Ming [verfasserIn] Ouyang, Wenbin [verfasserIn] Xu, Bugao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Stereo matching Hybrid cost aggregation Adaptive support region

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 79(2020), 31-32 vom: 06. Juni, Seite 23189-23202
Übergeordnetes Werk:	volume:79 ; year:2020 ; number:31-32 ; day:06 ; month:06 ; pages:23189-23202

Links:	Volltext

DOI / URN:	10.1007/s11042-020-09127-7

Katalog-ID:	SPR040672131

Internformat


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520			\|a Abstract Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be computed at each pixel for all disparities within the search range so that it can be used to evaluate pixel-to-pixel correspondence. Cost aggregation connects the matching cost with a certain neighbourhood to reduce mismatches by a supporting smoothness term. This paper presents a hybrid cost aggregation method to overcome mismatches caused by textureless surface, depth-discontinuity areas, inconsistent lightings in an image. The steps taken to aggregate costs for an energy function include adaptive support regions, multi-path aggregation, and adaptive penalties to generate a more accurate disparity map. Compared with two top-ranked stereo matching algorithms, the proposed algorithm yielded the disparity maps of the dataset in Middlebury benchmark V2 with smaller error ratios in depth-discontinuity regions.
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matchkey_str	article:15737721:2020----::yrdotgrgtofress
hierarchy_sort_str	2020
bklnumber	54.87
publishDate	2020
allfields	10.1007/s11042-020-09127-7 doi (DE-627)SPR040672131 (SPR)s11042-020-09127-7-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Yao, Ming verfasserin aut Hybrid cost aggregation for dense stereo matching 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be computed at each pixel for all disparities within the search range so that it can be used to evaluate pixel-to-pixel correspondence. Cost aggregation connects the matching cost with a certain neighbourhood to reduce mismatches by a supporting smoothness term. This paper presents a hybrid cost aggregation method to overcome mismatches caused by textureless surface, depth-discontinuity areas, inconsistent lightings in an image. The steps taken to aggregate costs for an energy function include adaptive support regions, multi-path aggregation, and adaptive penalties to generate a more accurate disparity map. Compared with two top-ranked stereo matching algorithms, the proposed algorithm yielded the disparity maps of the dataset in Middlebury benchmark V2 with smaller error ratios in depth-discontinuity regions. Stereo matching (dpeaa)DE-He213 Hybrid cost aggregation (dpeaa)DE-He213 Adaptive support region (dpeaa)DE-He213 Ouyang, Wenbin verfasserin aut Xu, Bugao verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 31-32 vom: 06. Juni, Seite 23189-23202 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:31-32 day:06 month:06 pages:23189-23202 https://dx.doi.org/10.1007/s11042-020-09127-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 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_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 79 2020 31-32 06 06 23189-23202
spelling	10.1007/s11042-020-09127-7 doi (DE-627)SPR040672131 (SPR)s11042-020-09127-7-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Yao, Ming verfasserin aut Hybrid cost aggregation for dense stereo matching 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be computed at each pixel for all disparities within the search range so that it can be used to evaluate pixel-to-pixel correspondence. Cost aggregation connects the matching cost with a certain neighbourhood to reduce mismatches by a supporting smoothness term. This paper presents a hybrid cost aggregation method to overcome mismatches caused by textureless surface, depth-discontinuity areas, inconsistent lightings in an image. The steps taken to aggregate costs for an energy function include adaptive support regions, multi-path aggregation, and adaptive penalties to generate a more accurate disparity map. Compared with two top-ranked stereo matching algorithms, the proposed algorithm yielded the disparity maps of the dataset in Middlebury benchmark V2 with smaller error ratios in depth-discontinuity regions. Stereo matching (dpeaa)DE-He213 Hybrid cost aggregation (dpeaa)DE-He213 Adaptive support region (dpeaa)DE-He213 Ouyang, Wenbin verfasserin aut Xu, Bugao verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 31-32 vom: 06. Juni, Seite 23189-23202 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:31-32 day:06 month:06 pages:23189-23202 https://dx.doi.org/10.1007/s11042-020-09127-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 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_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 79 2020 31-32 06 06 23189-23202
allfields_unstemmed	10.1007/s11042-020-09127-7 doi (DE-627)SPR040672131 (SPR)s11042-020-09127-7-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Yao, Ming verfasserin aut Hybrid cost aggregation for dense stereo matching 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be computed at each pixel for all disparities within the search range so that it can be used to evaluate pixel-to-pixel correspondence. Cost aggregation connects the matching cost with a certain neighbourhood to reduce mismatches by a supporting smoothness term. This paper presents a hybrid cost aggregation method to overcome mismatches caused by textureless surface, depth-discontinuity areas, inconsistent lightings in an image. The steps taken to aggregate costs for an energy function include adaptive support regions, multi-path aggregation, and adaptive penalties to generate a more accurate disparity map. Compared with two top-ranked stereo matching algorithms, the proposed algorithm yielded the disparity maps of the dataset in Middlebury benchmark V2 with smaller error ratios in depth-discontinuity regions. Stereo matching (dpeaa)DE-He213 Hybrid cost aggregation (dpeaa)DE-He213 Adaptive support region (dpeaa)DE-He213 Ouyang, Wenbin verfasserin aut Xu, Bugao verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 31-32 vom: 06. Juni, Seite 23189-23202 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:31-32 day:06 month:06 pages:23189-23202 https://dx.doi.org/10.1007/s11042-020-09127-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 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_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 79 2020 31-32 06 06 23189-23202
allfieldsGer	10.1007/s11042-020-09127-7 doi (DE-627)SPR040672131 (SPR)s11042-020-09127-7-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Yao, Ming verfasserin aut Hybrid cost aggregation for dense stereo matching 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be computed at each pixel for all disparities within the search range so that it can be used to evaluate pixel-to-pixel correspondence. Cost aggregation connects the matching cost with a certain neighbourhood to reduce mismatches by a supporting smoothness term. This paper presents a hybrid cost aggregation method to overcome mismatches caused by textureless surface, depth-discontinuity areas, inconsistent lightings in an image. The steps taken to aggregate costs for an energy function include adaptive support regions, multi-path aggregation, and adaptive penalties to generate a more accurate disparity map. Compared with two top-ranked stereo matching algorithms, the proposed algorithm yielded the disparity maps of the dataset in Middlebury benchmark V2 with smaller error ratios in depth-discontinuity regions. Stereo matching (dpeaa)DE-He213 Hybrid cost aggregation (dpeaa)DE-He213 Adaptive support region (dpeaa)DE-He213 Ouyang, Wenbin verfasserin aut Xu, Bugao verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 31-32 vom: 06. Juni, Seite 23189-23202 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:31-32 day:06 month:06 pages:23189-23202 https://dx.doi.org/10.1007/s11042-020-09127-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 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_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 79 2020 31-32 06 06 23189-23202
allfieldsSound	10.1007/s11042-020-09127-7 doi (DE-627)SPR040672131 (SPR)s11042-020-09127-7-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Yao, Ming verfasserin aut Hybrid cost aggregation for dense stereo matching 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be computed at each pixel for all disparities within the search range so that it can be used to evaluate pixel-to-pixel correspondence. Cost aggregation connects the matching cost with a certain neighbourhood to reduce mismatches by a supporting smoothness term. This paper presents a hybrid cost aggregation method to overcome mismatches caused by textureless surface, depth-discontinuity areas, inconsistent lightings in an image. The steps taken to aggregate costs for an energy function include adaptive support regions, multi-path aggregation, and adaptive penalties to generate a more accurate disparity map. Compared with two top-ranked stereo matching algorithms, the proposed algorithm yielded the disparity maps of the dataset in Middlebury benchmark V2 with smaller error ratios in depth-discontinuity regions. Stereo matching (dpeaa)DE-He213 Hybrid cost aggregation (dpeaa)DE-He213 Adaptive support region (dpeaa)DE-He213 Ouyang, Wenbin verfasserin aut Xu, Bugao verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 31-32 vom: 06. Juni, Seite 23189-23202 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:31-32 day:06 month:06 pages:23189-23202 https://dx.doi.org/10.1007/s11042-020-09127-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 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_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 79 2020 31-32 06 06 23189-23202
language	English
source	Enthalten in Multimedia tools and applications 79(2020), 31-32 vom: 06. Juni, Seite 23189-23202 volume:79 year:2020 number:31-32 day:06 month:06 pages:23189-23202
sourceStr	Enthalten in Multimedia tools and applications 79(2020), 31-32 vom: 06. Juni, Seite 23189-23202 volume:79 year:2020 number:31-32 day:06 month:06 pages:23189-23202
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Stereo matching Hybrid cost aggregation Adaptive support region
dewey-raw	070
isfreeaccess_bool	false
container_title	Multimedia tools and applications
authorswithroles_txt_mv	Yao, Ming @@aut@@ Ouyang, Wenbin @@aut@@ Xu, Bugao @@aut@@
publishDateDaySort_date	2020-06-06T00:00:00Z
hierarchy_top_id	27135030X
dewey-sort	270
id	SPR040672131
language_de	englisch
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author	Yao, Ming
spellingShingle	Yao, Ming ddc 070 bkl 54.87 misc Stereo matching misc Hybrid cost aggregation misc Adaptive support region Hybrid cost aggregation for dense stereo matching
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title	Hybrid cost aggregation for dense stereo matching
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title_full	Hybrid cost aggregation for dense stereo matching
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title_sort	hybrid cost aggregation for dense stereo matching
title_auth	Hybrid cost aggregation for dense stereo matching
abstract	Abstract Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be computed at each pixel for all disparities within the search range so that it can be used to evaluate pixel-to-pixel correspondence. Cost aggregation connects the matching cost with a certain neighbourhood to reduce mismatches by a supporting smoothness term. This paper presents a hybrid cost aggregation method to overcome mismatches caused by textureless surface, depth-discontinuity areas, inconsistent lightings in an image. The steps taken to aggregate costs for an energy function include adaptive support regions, multi-path aggregation, and adaptive penalties to generate a more accurate disparity map. Compared with two top-ranked stereo matching algorithms, the proposed algorithm yielded the disparity maps of the dataset in Middlebury benchmark V2 with smaller error ratios in depth-discontinuity regions.
abstractGer	Abstract Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be computed at each pixel for all disparities within the search range so that it can be used to evaluate pixel-to-pixel correspondence. Cost aggregation connects the matching cost with a certain neighbourhood to reduce mismatches by a supporting smoothness term. This paper presents a hybrid cost aggregation method to overcome mismatches caused by textureless surface, depth-discontinuity areas, inconsistent lightings in an image. The steps taken to aggregate costs for an energy function include adaptive support regions, multi-path aggregation, and adaptive penalties to generate a more accurate disparity map. Compared with two top-ranked stereo matching algorithms, the proposed algorithm yielded the disparity maps of the dataset in Middlebury benchmark V2 with smaller error ratios in depth-discontinuity regions.
abstract_unstemmed	Abstract Matching cost initialization and aggregation are two major steps in the stereo matching framework. For dense stereo matching, a matching cost needs to be computed at each pixel for all disparities within the search range so that it can be used to evaluate pixel-to-pixel correspondence. Cost aggregation connects the matching cost with a certain neighbourhood to reduce mismatches by a supporting smoothness term. This paper presents a hybrid cost aggregation method to overcome mismatches caused by textureless surface, depth-discontinuity areas, inconsistent lightings in an image. The steps taken to aggregate costs for an energy function include adaptive support regions, multi-path aggregation, and adaptive penalties to generate a more accurate disparity map. Compared with two top-ranked stereo matching algorithms, the proposed algorithm yielded the disparity maps of the dataset in Middlebury benchmark V2 with smaller error ratios in depth-discontinuity regions.
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title_short	Hybrid cost aggregation for dense stereo matching
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doi_str	10.1007/s11042-020-09127-7
up_date	2024-07-03T17:31:01.050Z
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