MIVI: multi-stage feature matching for infrared and visible image

Abstract The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching meth...
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Autor*in:	Di, Yide [verfasserIn] Liao, Yun Zhu, Kaijun Zhou, Hao Zhang, Yijia Duan, Qing Liu, Junhui Lu, Mingyu

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Feature matching Infrared image Visible image Transformer

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: The visual computer - Berlin : Springer, 1985, 40(2023), 3 vom: 24. Mai, Seite 1839-1851
Übergeordnetes Werk:	volume:40 ; year:2023 ; number:3 ; day:24 ; month:05 ; pages:1839-1851

Links:	Volltext

DOI / URN:	10.1007/s00371-023-02889-9

Katalog-ID:	SPR054778867

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520			\|a Abstract The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching method for infrared and visible images, named MIVI. Our proposed multi-stage matching architecture enables the model to capture both fine local feature details and remote dependencies, while our novel composite loss function optimizes the model at each stage and significantly improves the matching accuracy. Qualitative and quantitative experiments demonstrate that MIVI outperforms other excellent algorithms in terms of accuracy. The code will be released at: https://github.com/LiaoYun0x0/MIVI.
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allfields	10.1007/s00371-023-02889-9 doi (DE-627)SPR054778867 (SPR)s00371-023-02889-9-e DE-627 ger DE-627 rakwb eng Di, Yide verfasserin aut MIVI: multi-stage feature matching for infrared and visible image 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching method for infrared and visible images, named MIVI. Our proposed multi-stage matching architecture enables the model to capture both fine local feature details and remote dependencies, while our novel composite loss function optimizes the model at each stage and significantly improves the matching accuracy. Qualitative and quantitative experiments demonstrate that MIVI outperforms other excellent algorithms in terms of accuracy. The code will be released at: https://github.com/LiaoYun0x0/MIVI. Feature matching (dpeaa)DE-He213 Infrared image (dpeaa)DE-He213 Visible image (dpeaa)DE-He213 Transformer (dpeaa)DE-He213 Liao, Yun aut Zhu, Kaijun aut Zhou, Hao aut Zhang, Yijia aut Duan, Qing aut Liu, Junhui aut Lu, Mingyu (orcid)0000-0002-8663-9870 aut Enthalten in The visual computer Berlin : Springer, 1985 40(2023), 3 vom: 24. Mai, Seite 1839-1851 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:3 day:24 month:05 pages:1839-1851 https://dx.doi.org/10.1007/s00371-023-02889-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_267 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_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 AR 40 2023 3 24 05 1839-1851
spelling	10.1007/s00371-023-02889-9 doi (DE-627)SPR054778867 (SPR)s00371-023-02889-9-e DE-627 ger DE-627 rakwb eng Di, Yide verfasserin aut MIVI: multi-stage feature matching for infrared and visible image 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching method for infrared and visible images, named MIVI. Our proposed multi-stage matching architecture enables the model to capture both fine local feature details and remote dependencies, while our novel composite loss function optimizes the model at each stage and significantly improves the matching accuracy. Qualitative and quantitative experiments demonstrate that MIVI outperforms other excellent algorithms in terms of accuracy. The code will be released at: https://github.com/LiaoYun0x0/MIVI. Feature matching (dpeaa)DE-He213 Infrared image (dpeaa)DE-He213 Visible image (dpeaa)DE-He213 Transformer (dpeaa)DE-He213 Liao, Yun aut Zhu, Kaijun aut Zhou, Hao aut Zhang, Yijia aut Duan, Qing aut Liu, Junhui aut Lu, Mingyu (orcid)0000-0002-8663-9870 aut Enthalten in The visual computer Berlin : Springer, 1985 40(2023), 3 vom: 24. Mai, Seite 1839-1851 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:3 day:24 month:05 pages:1839-1851 https://dx.doi.org/10.1007/s00371-023-02889-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_267 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_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 AR 40 2023 3 24 05 1839-1851
allfields_unstemmed	10.1007/s00371-023-02889-9 doi (DE-627)SPR054778867 (SPR)s00371-023-02889-9-e DE-627 ger DE-627 rakwb eng Di, Yide verfasserin aut MIVI: multi-stage feature matching for infrared and visible image 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching method for infrared and visible images, named MIVI. Our proposed multi-stage matching architecture enables the model to capture both fine local feature details and remote dependencies, while our novel composite loss function optimizes the model at each stage and significantly improves the matching accuracy. Qualitative and quantitative experiments demonstrate that MIVI outperforms other excellent algorithms in terms of accuracy. The code will be released at: https://github.com/LiaoYun0x0/MIVI. Feature matching (dpeaa)DE-He213 Infrared image (dpeaa)DE-He213 Visible image (dpeaa)DE-He213 Transformer (dpeaa)DE-He213 Liao, Yun aut Zhu, Kaijun aut Zhou, Hao aut Zhang, Yijia aut Duan, Qing aut Liu, Junhui aut Lu, Mingyu (orcid)0000-0002-8663-9870 aut Enthalten in The visual computer Berlin : Springer, 1985 40(2023), 3 vom: 24. Mai, Seite 1839-1851 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:3 day:24 month:05 pages:1839-1851 https://dx.doi.org/10.1007/s00371-023-02889-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_267 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_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 AR 40 2023 3 24 05 1839-1851
allfieldsGer	10.1007/s00371-023-02889-9 doi (DE-627)SPR054778867 (SPR)s00371-023-02889-9-e DE-627 ger DE-627 rakwb eng Di, Yide verfasserin aut MIVI: multi-stage feature matching for infrared and visible image 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching method for infrared and visible images, named MIVI. Our proposed multi-stage matching architecture enables the model to capture both fine local feature details and remote dependencies, while our novel composite loss function optimizes the model at each stage and significantly improves the matching accuracy. Qualitative and quantitative experiments demonstrate that MIVI outperforms other excellent algorithms in terms of accuracy. The code will be released at: https://github.com/LiaoYun0x0/MIVI. Feature matching (dpeaa)DE-He213 Infrared image (dpeaa)DE-He213 Visible image (dpeaa)DE-He213 Transformer (dpeaa)DE-He213 Liao, Yun aut Zhu, Kaijun aut Zhou, Hao aut Zhang, Yijia aut Duan, Qing aut Liu, Junhui aut Lu, Mingyu (orcid)0000-0002-8663-9870 aut Enthalten in The visual computer Berlin : Springer, 1985 40(2023), 3 vom: 24. Mai, Seite 1839-1851 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:3 day:24 month:05 pages:1839-1851 https://dx.doi.org/10.1007/s00371-023-02889-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_267 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_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 AR 40 2023 3 24 05 1839-1851
allfieldsSound	10.1007/s00371-023-02889-9 doi (DE-627)SPR054778867 (SPR)s00371-023-02889-9-e DE-627 ger DE-627 rakwb eng Di, Yide verfasserin aut MIVI: multi-stage feature matching for infrared and visible image 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching method for infrared and visible images, named MIVI. Our proposed multi-stage matching architecture enables the model to capture both fine local feature details and remote dependencies, while our novel composite loss function optimizes the model at each stage and significantly improves the matching accuracy. Qualitative and quantitative experiments demonstrate that MIVI outperforms other excellent algorithms in terms of accuracy. The code will be released at: https://github.com/LiaoYun0x0/MIVI. Feature matching (dpeaa)DE-He213 Infrared image (dpeaa)DE-He213 Visible image (dpeaa)DE-He213 Transformer (dpeaa)DE-He213 Liao, Yun aut Zhu, Kaijun aut Zhou, Hao aut Zhang, Yijia aut Duan, Qing aut Liu, Junhui aut Lu, Mingyu (orcid)0000-0002-8663-9870 aut Enthalten in The visual computer Berlin : Springer, 1985 40(2023), 3 vom: 24. Mai, Seite 1839-1851 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:40 year:2023 number:3 day:24 month:05 pages:1839-1851 https://dx.doi.org/10.1007/s00371-023-02889-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_267 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_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 AR 40 2023 3 24 05 1839-1851
language	English
source	Enthalten in The visual computer 40(2023), 3 vom: 24. Mai, Seite 1839-1851 volume:40 year:2023 number:3 day:24 month:05 pages:1839-1851
sourceStr	Enthalten in The visual computer 40(2023), 3 vom: 24. Mai, Seite 1839-1851 volume:40 year:2023 number:3 day:24 month:05 pages:1839-1851
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Feature matching Infrared image Visible image Transformer
isfreeaccess_bool	false
container_title	The visual computer
authorswithroles_txt_mv	Di, Yide @@aut@@ Liao, Yun @@aut@@ Zhu, Kaijun @@aut@@ Zhou, Hao @@aut@@ Zhang, Yijia @@aut@@ Duan, Qing @@aut@@ Liu, Junhui @@aut@@ Lu, Mingyu @@aut@@
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author	Di, Yide
spellingShingle	Di, Yide misc Feature matching misc Infrared image misc Visible image misc Transformer MIVI: multi-stage feature matching for infrared and visible image
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topic_title	MIVI: multi-stage feature matching for infrared and visible image Feature matching (dpeaa)DE-He213 Infrared image (dpeaa)DE-He213 Visible image (dpeaa)DE-He213 Transformer (dpeaa)DE-He213
topic	misc Feature matching misc Infrared image misc Visible image misc Transformer
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title	MIVI: multi-stage feature matching for infrared and visible image
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title_full	MIVI: multi-stage feature matching for infrared and visible image
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author_browse	Di, Yide Liao, Yun Zhu, Kaijun Zhou, Hao Zhang, Yijia Duan, Qing Liu, Junhui Lu, Mingyu
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title_sort	mivi: multi-stage feature matching for infrared and visible image
title_auth	MIVI: multi-stage feature matching for infrared and visible image
abstract	Abstract The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching method for infrared and visible images, named MIVI. Our proposed multi-stage matching architecture enables the model to capture both fine local feature details and remote dependencies, while our novel composite loss function optimizes the model at each stage and significantly improves the matching accuracy. Qualitative and quantitative experiments demonstrate that MIVI outperforms other excellent algorithms in terms of accuracy. The code will be released at: https://github.com/LiaoYun0x0/MIVI. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching method for infrared and visible images, named MIVI. Our proposed multi-stage matching architecture enables the model to capture both fine local feature details and remote dependencies, while our novel composite loss function optimizes the model at each stage and significantly improves the matching accuracy. Qualitative and quantitative experiments demonstrate that MIVI outperforms other excellent algorithms in terms of accuracy. The code will be released at: https://github.com/LiaoYun0x0/MIVI. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract The matching of infrared and visible images has a wide range of applications across various fields. However, the large difference between these two types of images poses a significant challenge to achieving accurate feature matching. In this paper, we introduce a novel feature matching method for infrared and visible images, named MIVI. Our proposed multi-stage matching architecture enables the model to capture both fine local feature details and remote dependencies, while our novel composite loss function optimizes the model at each stage and significantly improves the matching accuracy. Qualitative and quantitative experiments demonstrate that MIVI outperforms other excellent algorithms in terms of accuracy. The code will be released at: https://github.com/LiaoYun0x0/MIVI. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	3
title_short	MIVI: multi-stage feature matching for infrared and visible image
url	https://dx.doi.org/10.1007/s00371-023-02889-9
remote_bool	true
author2	Liao, Yun Zhu, Kaijun Zhou, Hao Zhang, Yijia Duan, Qing Liu, Junhui Lu, Mingyu
author2Str	Liao, Yun Zhu, Kaijun Zhou, Hao Zhang, Yijia Duan, Qing Liu, Junhui Lu, Mingyu
ppnlink	254910734
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doi_str	10.1007/s00371-023-02889-9
up_date	2024-07-04T02:59:15.768Z
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score	7.399679

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MIVI: multi-stage feature matching for infrared and visible image

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