DNF: Feature Point Matching Pairs Filter Based on Descriptor Net

Wide-baseline image registration under out-of-plane rotation and larger viewpoint change is still challenging. Most of the commonly used matching algorithms are not invariant to affine transformation. They heavily rely on the local features of image patches and ignore global information, the mismatc...
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Autor*in:	Feng Ye [verfasserIn] Lingfeng Su [verfasserIn] Yizong Lai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Image registration mismatch removal wide baseline matching graph-based technique

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 87561-87573
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:87561-87573

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2925401

Katalog-ID:	DOAJ01423582X

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520			\|a Wide-baseline image registration under out-of-plane rotation and larger viewpoint change is still challenging. Most of the commonly used matching algorithms are not invariant to affine transformation. They heavily rely on the local features of image patches and ignore global information, the mismatch is inevitable and greatly affect the accuracy of image registration. To address this issue, we propose a feature point matching pair filter based on global spatial position correspondences of feature points, coined Descriptor Net Filter (DNF). We put forward two criteria to evaluate matching quality. One is the local matching quality computed by independent local feature, the other is the global matching quality relying on geometric network constraint. Combining the advantages of both local feature and large-scale geometric constraint, our method removes mismatches effectively. The experiments on both planar scenes and 3D objects from several standard datasets show that the DNF significantly enhances the matching precision and retains more correct matches as well.
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spelling	10.1109/ACCESS.2019.2925401 doi (DE-627)DOAJ01423582X (DE-599)DOAJ4a01547aac5a44049ef7403a7238fa1f DE-627 ger DE-627 rakwb eng TK1-9971 Feng Ye verfasserin aut DNF: Feature Point Matching Pairs Filter Based on Descriptor Net 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wide-baseline image registration under out-of-plane rotation and larger viewpoint change is still challenging. Most of the commonly used matching algorithms are not invariant to affine transformation. They heavily rely on the local features of image patches and ignore global information, the mismatch is inevitable and greatly affect the accuracy of image registration. To address this issue, we propose a feature point matching pair filter based on global spatial position correspondences of feature points, coined Descriptor Net Filter (DNF). We put forward two criteria to evaluate matching quality. One is the local matching quality computed by independent local feature, the other is the global matching quality relying on geometric network constraint. Combining the advantages of both local feature and large-scale geometric constraint, our method removes mismatches effectively. The experiments on both planar scenes and 3D objects from several standard datasets show that the DNF significantly enhances the matching precision and retains more correct matches as well. Image registration mismatch removal wide baseline matching graph-based technique Electrical engineering. Electronics. Nuclear engineering Lingfeng Su verfasserin aut Yizong Lai verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 87561-87573 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:87561-87573 https://doi.org/10.1109/ACCESS.2019.2925401 kostenfrei https://doaj.org/article/4a01547aac5a44049ef7403a7238fa1f kostenfrei https://ieeexplore.ieee.org/document/8747438/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 87561-87573
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allfieldsSound	10.1109/ACCESS.2019.2925401 doi (DE-627)DOAJ01423582X (DE-599)DOAJ4a01547aac5a44049ef7403a7238fa1f DE-627 ger DE-627 rakwb eng TK1-9971 Feng Ye verfasserin aut DNF: Feature Point Matching Pairs Filter Based on Descriptor Net 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wide-baseline image registration under out-of-plane rotation and larger viewpoint change is still challenging. Most of the commonly used matching algorithms are not invariant to affine transformation. They heavily rely on the local features of image patches and ignore global information, the mismatch is inevitable and greatly affect the accuracy of image registration. To address this issue, we propose a feature point matching pair filter based on global spatial position correspondences of feature points, coined Descriptor Net Filter (DNF). We put forward two criteria to evaluate matching quality. One is the local matching quality computed by independent local feature, the other is the global matching quality relying on geometric network constraint. Combining the advantages of both local feature and large-scale geometric constraint, our method removes mismatches effectively. The experiments on both planar scenes and 3D objects from several standard datasets show that the DNF significantly enhances the matching precision and retains more correct matches as well. Image registration mismatch removal wide baseline matching graph-based technique Electrical engineering. Electronics. Nuclear engineering Lingfeng Su verfasserin aut Yizong Lai verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 87561-87573 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:87561-87573 https://doi.org/10.1109/ACCESS.2019.2925401 kostenfrei https://doaj.org/article/4a01547aac5a44049ef7403a7238fa1f kostenfrei https://ieeexplore.ieee.org/document/8747438/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 87561-87573
language	English
source	In IEEE Access 7(2019), Seite 87561-87573 volume:7 year:2019 pages:87561-87573
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topic_facet	Image registration mismatch removal wide baseline matching graph-based technique Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Feng Ye @@aut@@ Lingfeng Su @@aut@@ Yizong Lai @@aut@@
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callnumber-first	T - Technology
author	Feng Ye
spellingShingle	Feng Ye misc TK1-9971 misc Image registration misc mismatch removal misc wide baseline matching misc graph-based technique misc Electrical engineering. Electronics. Nuclear engineering DNF: Feature Point Matching Pairs Filter Based on Descriptor Net
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topic_title	TK1-9971 DNF: Feature Point Matching Pairs Filter Based on Descriptor Net Image registration mismatch removal wide baseline matching graph-based technique
topic	misc TK1-9971 misc Image registration misc mismatch removal misc wide baseline matching misc graph-based technique misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Image registration misc mismatch removal misc wide baseline matching misc graph-based technique misc Electrical engineering. Electronics. Nuclear engineering
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title	DNF: Feature Point Matching Pairs Filter Based on Descriptor Net
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title_full	DNF: Feature Point Matching Pairs Filter Based on Descriptor Net
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title_sort	dnf: feature point matching pairs filter based on descriptor net
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title_auth	DNF: Feature Point Matching Pairs Filter Based on Descriptor Net
abstract	Wide-baseline image registration under out-of-plane rotation and larger viewpoint change is still challenging. Most of the commonly used matching algorithms are not invariant to affine transformation. They heavily rely on the local features of image patches and ignore global information, the mismatch is inevitable and greatly affect the accuracy of image registration. To address this issue, we propose a feature point matching pair filter based on global spatial position correspondences of feature points, coined Descriptor Net Filter (DNF). We put forward two criteria to evaluate matching quality. One is the local matching quality computed by independent local feature, the other is the global matching quality relying on geometric network constraint. Combining the advantages of both local feature and large-scale geometric constraint, our method removes mismatches effectively. The experiments on both planar scenes and 3D objects from several standard datasets show that the DNF significantly enhances the matching precision and retains more correct matches as well.
abstractGer	Wide-baseline image registration under out-of-plane rotation and larger viewpoint change is still challenging. Most of the commonly used matching algorithms are not invariant to affine transformation. They heavily rely on the local features of image patches and ignore global information, the mismatch is inevitable and greatly affect the accuracy of image registration. To address this issue, we propose a feature point matching pair filter based on global spatial position correspondences of feature points, coined Descriptor Net Filter (DNF). We put forward two criteria to evaluate matching quality. One is the local matching quality computed by independent local feature, the other is the global matching quality relying on geometric network constraint. Combining the advantages of both local feature and large-scale geometric constraint, our method removes mismatches effectively. The experiments on both planar scenes and 3D objects from several standard datasets show that the DNF significantly enhances the matching precision and retains more correct matches as well.
abstract_unstemmed	Wide-baseline image registration under out-of-plane rotation and larger viewpoint change is still challenging. Most of the commonly used matching algorithms are not invariant to affine transformation. They heavily rely on the local features of image patches and ignore global information, the mismatch is inevitable and greatly affect the accuracy of image registration. To address this issue, we propose a feature point matching pair filter based on global spatial position correspondences of feature points, coined Descriptor Net Filter (DNF). We put forward two criteria to evaluate matching quality. One is the local matching quality computed by independent local feature, the other is the global matching quality relying on geometric network constraint. Combining the advantages of both local feature and large-scale geometric constraint, our method removes mismatches effectively. The experiments on both planar scenes and 3D objects from several standard datasets show that the DNF significantly enhances the matching precision and retains more correct matches as well.
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title_short	DNF: Feature Point Matching Pairs Filter Based on Descriptor Net
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