An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds

Measurements by many multi-sensor systems can be considered as point-clouds. One such system is the tracker for the PANDA experiment. Charged particles passing through the tracker produce patterns representing their paths. We present a new, graph-based, attribute-space morphological connected filter...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Babai, M. [verfasserIn] Kalantar-Nayestanaki, N. [verfasserIn] Messchendorp, J.G. [verfasserIn] Wilkinson, M.H.F. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Graph-based image analysis Attribute-space connectivity Orientation-based segmentation Irregular graph morphology Graph morphology Orientation-based path merging Sub-atomic particle tracking Straw tube tracker (STT)

Übergeordnetes Werk:	Enthalten in: Pattern recognition - Amsterdam : Elsevier, 1968, 106
Übergeordnetes Werk:	volume:106

DOI / URN:	10.1016/j.patcog.2020.107467

Katalog-ID:	ELV004298179

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520			\|a Measurements by many multi-sensor systems can be considered as point-clouds. One such system is the tracker for the PANDA experiment. Charged particles passing through the tracker produce patterns representing their paths. We present a new, graph-based, attribute-space morphological connected filter for reconstructing particle paths through such a detector. We introduce the concept of attribute-spaces and attribute-space connected filters on graphs, rather than binary images and show a new processing scheme to reduce the size of the memory required to store the attribute-space representations of binary images and graphs. The result is an O(Nlog (N)) algorithm with a total recognition error of approximately 0.10, a significant improvement compared to our previous state-of-the-art O(N 2) algorithm with a total error of 0.17.
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650		4	\|a Attribute-space connectivity
650		4	\|a Orientation-based segmentation
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650		4	\|a Orientation-based path merging
650		4	\|a Sub-atomic particle tracking
650		4	\|a Straw tube tracker (STT)
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700	1		\|a Messchendorp, J.G. \|e verfasserin \|4 aut
700	1		\|a Wilkinson, M.H.F. \|e verfasserin \|4 aut
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author_variant	m b mb n k n nkn j m jm m w mw
matchkey_str	babaimkalantarnayestanakinmesschendorpjg:2020----:nfiinatiuepccnetditrnrpsoeos
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allfields	10.1016/j.patcog.2020.107467 doi (DE-627)ELV004298179 (ELSEVIER)S0031-3203(20)30270-3 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Babai, M. verfasserin (orcid)0000-0001-6139-2878 aut An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Measurements by many multi-sensor systems can be considered as point-clouds. One such system is the tracker for the PANDA experiment. Charged particles passing through the tracker produce patterns representing their paths. We present a new, graph-based, attribute-space morphological connected filter for reconstructing particle paths through such a detector. We introduce the concept of attribute-spaces and attribute-space connected filters on graphs, rather than binary images and show a new processing scheme to reduce the size of the memory required to store the attribute-space representations of binary images and graphs. The result is an O(Nlog (N)) algorithm with a total recognition error of approximately 0.10, a significant improvement compared to our previous state-of-the-art O(N 2) algorithm with a total error of 0.17. Graph-based image analysis Attribute-space connectivity Orientation-based segmentation Irregular graph morphology Graph morphology Orientation-based path merging Sub-atomic particle tracking Straw tube tracker (STT) Kalantar-Nayestanaki, N. verfasserin aut Messchendorp, J.G. verfasserin aut Wilkinson, M.H.F. verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 106 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:106 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 106
spelling	10.1016/j.patcog.2020.107467 doi (DE-627)ELV004298179 (ELSEVIER)S0031-3203(20)30270-3 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Babai, M. verfasserin (orcid)0000-0001-6139-2878 aut An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Measurements by many multi-sensor systems can be considered as point-clouds. One such system is the tracker for the PANDA experiment. Charged particles passing through the tracker produce patterns representing their paths. We present a new, graph-based, attribute-space morphological connected filter for reconstructing particle paths through such a detector. We introduce the concept of attribute-spaces and attribute-space connected filters on graphs, rather than binary images and show a new processing scheme to reduce the size of the memory required to store the attribute-space representations of binary images and graphs. The result is an O(Nlog (N)) algorithm with a total recognition error of approximately 0.10, a significant improvement compared to our previous state-of-the-art O(N 2) algorithm with a total error of 0.17. Graph-based image analysis Attribute-space connectivity Orientation-based segmentation Irregular graph morphology Graph morphology Orientation-based path merging Sub-atomic particle tracking Straw tube tracker (STT) Kalantar-Nayestanaki, N. verfasserin aut Messchendorp, J.G. verfasserin aut Wilkinson, M.H.F. verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 106 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:106 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 106
allfields_unstemmed	10.1016/j.patcog.2020.107467 doi (DE-627)ELV004298179 (ELSEVIER)S0031-3203(20)30270-3 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Babai, M. verfasserin (orcid)0000-0001-6139-2878 aut An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Measurements by many multi-sensor systems can be considered as point-clouds. One such system is the tracker for the PANDA experiment. Charged particles passing through the tracker produce patterns representing their paths. We present a new, graph-based, attribute-space morphological connected filter for reconstructing particle paths through such a detector. We introduce the concept of attribute-spaces and attribute-space connected filters on graphs, rather than binary images and show a new processing scheme to reduce the size of the memory required to store the attribute-space representations of binary images and graphs. The result is an O(Nlog (N)) algorithm with a total recognition error of approximately 0.10, a significant improvement compared to our previous state-of-the-art O(N 2) algorithm with a total error of 0.17. Graph-based image analysis Attribute-space connectivity Orientation-based segmentation Irregular graph morphology Graph morphology Orientation-based path merging Sub-atomic particle tracking Straw tube tracker (STT) Kalantar-Nayestanaki, N. verfasserin aut Messchendorp, J.G. verfasserin aut Wilkinson, M.H.F. verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 106 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:106 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 106
allfieldsGer	10.1016/j.patcog.2020.107467 doi (DE-627)ELV004298179 (ELSEVIER)S0031-3203(20)30270-3 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Babai, M. verfasserin (orcid)0000-0001-6139-2878 aut An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Measurements by many multi-sensor systems can be considered as point-clouds. One such system is the tracker for the PANDA experiment. Charged particles passing through the tracker produce patterns representing their paths. We present a new, graph-based, attribute-space morphological connected filter for reconstructing particle paths through such a detector. We introduce the concept of attribute-spaces and attribute-space connected filters on graphs, rather than binary images and show a new processing scheme to reduce the size of the memory required to store the attribute-space representations of binary images and graphs. The result is an O(Nlog (N)) algorithm with a total recognition error of approximately 0.10, a significant improvement compared to our previous state-of-the-art O(N 2) algorithm with a total error of 0.17. Graph-based image analysis Attribute-space connectivity Orientation-based segmentation Irregular graph morphology Graph morphology Orientation-based path merging Sub-atomic particle tracking Straw tube tracker (STT) Kalantar-Nayestanaki, N. verfasserin aut Messchendorp, J.G. verfasserin aut Wilkinson, M.H.F. verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 106 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:106 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 106
allfieldsSound	10.1016/j.patcog.2020.107467 doi (DE-627)ELV004298179 (ELSEVIER)S0031-3203(20)30270-3 DE-627 ger DE-627 rda eng 000 150 DE-600 54.74 bkl Babai, M. verfasserin (orcid)0000-0001-6139-2878 aut An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Measurements by many multi-sensor systems can be considered as point-clouds. One such system is the tracker for the PANDA experiment. Charged particles passing through the tracker produce patterns representing their paths. We present a new, graph-based, attribute-space morphological connected filter for reconstructing particle paths through such a detector. We introduce the concept of attribute-spaces and attribute-space connected filters on graphs, rather than binary images and show a new processing scheme to reduce the size of the memory required to store the attribute-space representations of binary images and graphs. The result is an O(Nlog (N)) algorithm with a total recognition error of approximately 0.10, a significant improvement compared to our previous state-of-the-art O(N 2) algorithm with a total error of 0.17. Graph-based image analysis Attribute-space connectivity Orientation-based segmentation Irregular graph morphology Graph morphology Orientation-based path merging Sub-atomic particle tracking Straw tube tracker (STT) Kalantar-Nayestanaki, N. verfasserin aut Messchendorp, J.G. verfasserin aut Wilkinson, M.H.F. verfasserin aut Enthalten in Pattern recognition Amsterdam : Elsevier, 1968 106 Online-Ressource (DE-627)265784131 (DE-600)1466343-0 (DE-576)101177364 nnns volume:106 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 106
language	English
source	Enthalten in Pattern recognition 106 volume:106
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topic_facet	Graph-based image analysis Attribute-space connectivity Orientation-based segmentation Irregular graph morphology Graph morphology Orientation-based path merging Sub-atomic particle tracking Straw tube tracker (STT)
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authorswithroles_txt_mv	Babai, M. @@aut@@ Kalantar-Nayestanaki, N. @@aut@@ Messchendorp, J.G. @@aut@@ Wilkinson, M.H.F. @@aut@@
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author	Babai, M.
spellingShingle	Babai, M. ddc 000 bkl 54.74 misc Graph-based image analysis misc Attribute-space connectivity misc Orientation-based segmentation misc Irregular graph morphology misc Graph morphology misc Orientation-based path merging misc Sub-atomic particle tracking misc Straw tube tracker (STT) An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds
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topic_title	000 150 DE-600 54.74 bkl An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds Graph-based image analysis Attribute-space connectivity Orientation-based segmentation Irregular graph morphology Graph morphology Orientation-based path merging Sub-atomic particle tracking Straw tube tracker (STT)
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title	An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds
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title_full	An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds
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title_sort	an efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds
title_auth	An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds
abstract	Measurements by many multi-sensor systems can be considered as point-clouds. One such system is the tracker for the PANDA experiment. Charged particles passing through the tracker produce patterns representing their paths. We present a new, graph-based, attribute-space morphological connected filter for reconstructing particle paths through such a detector. We introduce the concept of attribute-spaces and attribute-space connected filters on graphs, rather than binary images and show a new processing scheme to reduce the size of the memory required to store the attribute-space representations of binary images and graphs. The result is an O(Nlog (N)) algorithm with a total recognition error of approximately 0.10, a significant improvement compared to our previous state-of-the-art O(N 2) algorithm with a total error of 0.17.
abstractGer	Measurements by many multi-sensor systems can be considered as point-clouds. One such system is the tracker for the PANDA experiment. Charged particles passing through the tracker produce patterns representing their paths. We present a new, graph-based, attribute-space morphological connected filter for reconstructing particle paths through such a detector. We introduce the concept of attribute-spaces and attribute-space connected filters on graphs, rather than binary images and show a new processing scheme to reduce the size of the memory required to store the attribute-space representations of binary images and graphs. The result is an O(Nlog (N)) algorithm with a total recognition error of approximately 0.10, a significant improvement compared to our previous state-of-the-art O(N 2) algorithm with a total error of 0.17.
abstract_unstemmed	Measurements by many multi-sensor systems can be considered as point-clouds. One such system is the tracker for the PANDA experiment. Charged particles passing through the tracker produce patterns representing their paths. We present a new, graph-based, attribute-space morphological connected filter for reconstructing particle paths through such a detector. We introduce the concept of attribute-spaces and attribute-space connected filters on graphs, rather than binary images and show a new processing scheme to reduce the size of the memory required to store the attribute-space representations of binary images and graphs. The result is an O(Nlog (N)) algorithm with a total recognition error of approximately 0.10, a significant improvement compared to our previous state-of-the-art O(N 2) algorithm with a total error of 0.17.
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title_short	An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds
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author2	Kalantar-Nayestanaki, N. Messchendorp, J.G. Wilkinson, M.H.F.
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up_date	2024-07-06T22:31:22.096Z
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An efficient attribute-space connected filter on graphs to reconstruct paths in point-clouds

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