A visible-light and infrared video database for performance evaluation of video/image fusion methods
Abstract In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, ther...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ellmauthaler, Andreas [verfasserIn] Pagliari, Carla L. [verfasserIn] da Silva, Eduardo A. B. [verfasserIn] Gois, Jonathan N. [verfasserIn] Neves, Sergio R. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Multidimensional systems and signal processing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990, 30(2017), 1 vom: 27. Dez., Seite 119-143 |
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| Übergeordnetes Werk: |
volume:30 ; year:2017 ; number:1 ; day:27 ; month:12 ; pages:119-143 |
| Links: |
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| DOI / URN: |
10.1007/s11045-017-0548-y |
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| Katalog-ID: |
SPR016056663 |
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| 520 | |a Abstract In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, there is a particular shortage of databases with registered and synchronized videos from the infrared and visible-light spectra suitable for image/video fusion research. To address this need we recorded an image/video fusion database using infrared and visible-light cameras under varying illumination conditions. Moreover, different scenarios have been defined to better challenge the fusion methods, with various contexts and contents providing a wide variety of meaningful data for fusion purposes, including non-planar scenes, where objects appear on different depth planes. However, there are several difficulties in creating datasets for research in infrared/visible-light image fusion. Camera calibration, registration, and synchronization can be listed as important steps of this task. In particular, image registration between imagery from sensors of different spectral bands imposes additional difficulties, as it is very challenging to solve the correspondence problem between such images. Motivated by these challenges, this work introduces a novel spatiotemporal video registration method capable of generating registered and temporally aligned infrared/visible-light video sequences. The proposed workflow improves the registration accuracy when compared to the state-of-the art. By applying the proposed methodology to the recorded database we have generated the visible-light and infrared video database for image fusion, a publicly available database to be used by the research community to test and benchmark fusion schemes. | ||
| 650 | 4 | |a Infrared/visible image/video database |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image registration |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image fusion |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Camera calibration |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Pagliari, Carla L. |e verfasserin |4 aut | |
| 700 | 1 | |a da Silva, Eduardo A. B. |e verfasserin |4 aut | |
| 700 | 1 | |a Gois, Jonathan N. |e verfasserin |4 aut | |
| 700 | 1 | |a Neves, Sergio R. |e verfasserin |4 aut | |
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10.1007/s11045-017-0548-y doi (DE-627)SPR016056663 (SPR)s11045-017-0548-y-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Ellmauthaler, Andreas verfasserin aut A visible-light and infrared video database for performance evaluation of video/image fusion methods 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, there is a particular shortage of databases with registered and synchronized videos from the infrared and visible-light spectra suitable for image/video fusion research. To address this need we recorded an image/video fusion database using infrared and visible-light cameras under varying illumination conditions. Moreover, different scenarios have been defined to better challenge the fusion methods, with various contexts and contents providing a wide variety of meaningful data for fusion purposes, including non-planar scenes, where objects appear on different depth planes. However, there are several difficulties in creating datasets for research in infrared/visible-light image fusion. Camera calibration, registration, and synchronization can be listed as important steps of this task. In particular, image registration between imagery from sensors of different spectral bands imposes additional difficulties, as it is very challenging to solve the correspondence problem between such images. Motivated by these challenges, this work introduces a novel spatiotemporal video registration method capable of generating registered and temporally aligned infrared/visible-light video sequences. The proposed workflow improves the registration accuracy when compared to the state-of-the art. By applying the proposed methodology to the recorded database we have generated the visible-light and infrared video database for image fusion, a publicly available database to be used by the research community to test and benchmark fusion schemes. Infrared/visible image/video database (dpeaa)DE-He213 Image registration (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Camera calibration (dpeaa)DE-He213 Pagliari, Carla L. verfasserin aut da Silva, Eduardo A. B. verfasserin aut Gois, Jonathan N. verfasserin aut Neves, Sergio R. verfasserin aut Enthalten in Multidimensional systems and signal processing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 30(2017), 1 vom: 27. Dez., Seite 119-143 (DE-627)271178191 (DE-600)1479232-1 1573-0824 nnns volume:30 year:2017 number:1 day:27 month:12 pages:119-143 https://dx.doi.org/10.1007/s11045-017-0548-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 30 2017 1 27 12 119-143 |
| spelling |
10.1007/s11045-017-0548-y doi (DE-627)SPR016056663 (SPR)s11045-017-0548-y-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Ellmauthaler, Andreas verfasserin aut A visible-light and infrared video database for performance evaluation of video/image fusion methods 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, there is a particular shortage of databases with registered and synchronized videos from the infrared and visible-light spectra suitable for image/video fusion research. To address this need we recorded an image/video fusion database using infrared and visible-light cameras under varying illumination conditions. Moreover, different scenarios have been defined to better challenge the fusion methods, with various contexts and contents providing a wide variety of meaningful data for fusion purposes, including non-planar scenes, where objects appear on different depth planes. However, there are several difficulties in creating datasets for research in infrared/visible-light image fusion. Camera calibration, registration, and synchronization can be listed as important steps of this task. In particular, image registration between imagery from sensors of different spectral bands imposes additional difficulties, as it is very challenging to solve the correspondence problem between such images. Motivated by these challenges, this work introduces a novel spatiotemporal video registration method capable of generating registered and temporally aligned infrared/visible-light video sequences. The proposed workflow improves the registration accuracy when compared to the state-of-the art. By applying the proposed methodology to the recorded database we have generated the visible-light and infrared video database for image fusion, a publicly available database to be used by the research community to test and benchmark fusion schemes. Infrared/visible image/video database (dpeaa)DE-He213 Image registration (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Camera calibration (dpeaa)DE-He213 Pagliari, Carla L. verfasserin aut da Silva, Eduardo A. B. verfasserin aut Gois, Jonathan N. verfasserin aut Neves, Sergio R. verfasserin aut Enthalten in Multidimensional systems and signal processing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 30(2017), 1 vom: 27. Dez., Seite 119-143 (DE-627)271178191 (DE-600)1479232-1 1573-0824 nnns volume:30 year:2017 number:1 day:27 month:12 pages:119-143 https://dx.doi.org/10.1007/s11045-017-0548-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 30 2017 1 27 12 119-143 |
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10.1007/s11045-017-0548-y doi (DE-627)SPR016056663 (SPR)s11045-017-0548-y-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Ellmauthaler, Andreas verfasserin aut A visible-light and infrared video database for performance evaluation of video/image fusion methods 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, there is a particular shortage of databases with registered and synchronized videos from the infrared and visible-light spectra suitable for image/video fusion research. To address this need we recorded an image/video fusion database using infrared and visible-light cameras under varying illumination conditions. Moreover, different scenarios have been defined to better challenge the fusion methods, with various contexts and contents providing a wide variety of meaningful data for fusion purposes, including non-planar scenes, where objects appear on different depth planes. However, there are several difficulties in creating datasets for research in infrared/visible-light image fusion. Camera calibration, registration, and synchronization can be listed as important steps of this task. In particular, image registration between imagery from sensors of different spectral bands imposes additional difficulties, as it is very challenging to solve the correspondence problem between such images. Motivated by these challenges, this work introduces a novel spatiotemporal video registration method capable of generating registered and temporally aligned infrared/visible-light video sequences. The proposed workflow improves the registration accuracy when compared to the state-of-the art. By applying the proposed methodology to the recorded database we have generated the visible-light and infrared video database for image fusion, a publicly available database to be used by the research community to test and benchmark fusion schemes. Infrared/visible image/video database (dpeaa)DE-He213 Image registration (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Camera calibration (dpeaa)DE-He213 Pagliari, Carla L. verfasserin aut da Silva, Eduardo A. B. verfasserin aut Gois, Jonathan N. verfasserin aut Neves, Sergio R. verfasserin aut Enthalten in Multidimensional systems and signal processing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 30(2017), 1 vom: 27. Dez., Seite 119-143 (DE-627)271178191 (DE-600)1479232-1 1573-0824 nnns volume:30 year:2017 number:1 day:27 month:12 pages:119-143 https://dx.doi.org/10.1007/s11045-017-0548-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 30 2017 1 27 12 119-143 |
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10.1007/s11045-017-0548-y doi (DE-627)SPR016056663 (SPR)s11045-017-0548-y-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Ellmauthaler, Andreas verfasserin aut A visible-light and infrared video database for performance evaluation of video/image fusion methods 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, there is a particular shortage of databases with registered and synchronized videos from the infrared and visible-light spectra suitable for image/video fusion research. To address this need we recorded an image/video fusion database using infrared and visible-light cameras under varying illumination conditions. Moreover, different scenarios have been defined to better challenge the fusion methods, with various contexts and contents providing a wide variety of meaningful data for fusion purposes, including non-planar scenes, where objects appear on different depth planes. However, there are several difficulties in creating datasets for research in infrared/visible-light image fusion. Camera calibration, registration, and synchronization can be listed as important steps of this task. In particular, image registration between imagery from sensors of different spectral bands imposes additional difficulties, as it is very challenging to solve the correspondence problem between such images. Motivated by these challenges, this work introduces a novel spatiotemporal video registration method capable of generating registered and temporally aligned infrared/visible-light video sequences. The proposed workflow improves the registration accuracy when compared to the state-of-the art. By applying the proposed methodology to the recorded database we have generated the visible-light and infrared video database for image fusion, a publicly available database to be used by the research community to test and benchmark fusion schemes. Infrared/visible image/video database (dpeaa)DE-He213 Image registration (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Camera calibration (dpeaa)DE-He213 Pagliari, Carla L. verfasserin aut da Silva, Eduardo A. B. verfasserin aut Gois, Jonathan N. verfasserin aut Neves, Sergio R. verfasserin aut Enthalten in Multidimensional systems and signal processing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 30(2017), 1 vom: 27. Dez., Seite 119-143 (DE-627)271178191 (DE-600)1479232-1 1573-0824 nnns volume:30 year:2017 number:1 day:27 month:12 pages:119-143 https://dx.doi.org/10.1007/s11045-017-0548-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 30 2017 1 27 12 119-143 |
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10.1007/s11045-017-0548-y doi (DE-627)SPR016056663 (SPR)s11045-017-0548-y-e DE-627 ger DE-627 rakwb eng 510 ASE 31.00 bkl Ellmauthaler, Andreas verfasserin aut A visible-light and infrared video database for performance evaluation of video/image fusion methods 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, there is a particular shortage of databases with registered and synchronized videos from the infrared and visible-light spectra suitable for image/video fusion research. To address this need we recorded an image/video fusion database using infrared and visible-light cameras under varying illumination conditions. Moreover, different scenarios have been defined to better challenge the fusion methods, with various contexts and contents providing a wide variety of meaningful data for fusion purposes, including non-planar scenes, where objects appear on different depth planes. However, there are several difficulties in creating datasets for research in infrared/visible-light image fusion. Camera calibration, registration, and synchronization can be listed as important steps of this task. In particular, image registration between imagery from sensors of different spectral bands imposes additional difficulties, as it is very challenging to solve the correspondence problem between such images. Motivated by these challenges, this work introduces a novel spatiotemporal video registration method capable of generating registered and temporally aligned infrared/visible-light video sequences. The proposed workflow improves the registration accuracy when compared to the state-of-the art. By applying the proposed methodology to the recorded database we have generated the visible-light and infrared video database for image fusion, a publicly available database to be used by the research community to test and benchmark fusion schemes. Infrared/visible image/video database (dpeaa)DE-He213 Image registration (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Camera calibration (dpeaa)DE-He213 Pagliari, Carla L. verfasserin aut da Silva, Eduardo A. B. verfasserin aut Gois, Jonathan N. verfasserin aut Neves, Sergio R. verfasserin aut Enthalten in Multidimensional systems and signal processing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 30(2017), 1 vom: 27. Dez., Seite 119-143 (DE-627)271178191 (DE-600)1479232-1 1573-0824 nnns volume:30 year:2017 number:1 day:27 month:12 pages:119-143 https://dx.doi.org/10.1007/s11045-017-0548-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 30 2017 1 27 12 119-143 |
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Enthalten in Multidimensional systems and signal processing 30(2017), 1 vom: 27. Dez., Seite 119-143 volume:30 year:2017 number:1 day:27 month:12 pages:119-143 |
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Enthalten in Multidimensional systems and signal processing 30(2017), 1 vom: 27. Dez., Seite 119-143 volume:30 year:2017 number:1 day:27 month:12 pages:119-143 |
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Infrared/visible image/video database Image registration Image fusion Camera calibration |
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Multidimensional systems and signal processing |
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Ellmauthaler, Andreas @@aut@@ Pagliari, Carla L. @@aut@@ da Silva, Eduardo A. B. @@aut@@ Gois, Jonathan N. @@aut@@ Neves, Sergio R. @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR016056663</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111024941.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11045-017-0548-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR016056663</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11045-017-0548-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ellmauthaler, Andreas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A visible-light and infrared video database for performance evaluation of video/image fusion methods</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, there is a particular shortage of databases with registered and synchronized videos from the infrared and visible-light spectra suitable for image/video fusion research. To address this need we recorded an image/video fusion database using infrared and visible-light cameras under varying illumination conditions. Moreover, different scenarios have been defined to better challenge the fusion methods, with various contexts and contents providing a wide variety of meaningful data for fusion purposes, including non-planar scenes, where objects appear on different depth planes. However, there are several difficulties in creating datasets for research in infrared/visible-light image fusion. Camera calibration, registration, and synchronization can be listed as important steps of this task. In particular, image registration between imagery from sensors of different spectral bands imposes additional difficulties, as it is very challenging to solve the correspondence problem between such images. Motivated by these challenges, this work introduces a novel spatiotemporal video registration method capable of generating registered and temporally aligned infrared/visible-light video sequences. The proposed workflow improves the registration accuracy when compared to the state-of-the art. By applying the proposed methodology to the recorded database we have generated the visible-light and infrared video database for image fusion, a publicly available database to be used by the research community to test and benchmark fusion schemes.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Infrared/visible image/video database</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image registration</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image fusion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Camera calibration</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pagliari, Carla L.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">da Silva, Eduardo A. 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Ellmauthaler, Andreas |
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Ellmauthaler, Andreas ddc 510 bkl 31.00 misc Infrared/visible image/video database misc Image registration misc Image fusion misc Camera calibration A visible-light and infrared video database for performance evaluation of video/image fusion methods |
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510 ASE 31.00 bkl A visible-light and infrared video database for performance evaluation of video/image fusion methods Infrared/visible image/video database (dpeaa)DE-He213 Image registration (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Camera calibration (dpeaa)DE-He213 |
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ddc 510 bkl 31.00 misc Infrared/visible image/video database misc Image registration misc Image fusion misc Camera calibration |
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ddc 510 bkl 31.00 misc Infrared/visible image/video database misc Image registration misc Image fusion misc Camera calibration |
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ddc 510 bkl 31.00 misc Infrared/visible image/video database misc Image registration misc Image fusion misc Camera calibration |
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A visible-light and infrared video database for performance evaluation of video/image fusion methods |
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Ellmauthaler, Andreas Pagliari, Carla L. da Silva, Eduardo A. B. Gois, Jonathan N. Neves, Sergio R. |
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visible-light and infrared video database for performance evaluation of video/image fusion methods |
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A visible-light and infrared video database for performance evaluation of video/image fusion methods |
| abstract |
Abstract In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, there is a particular shortage of databases with registered and synchronized videos from the infrared and visible-light spectra suitable for image/video fusion research. To address this need we recorded an image/video fusion database using infrared and visible-light cameras under varying illumination conditions. Moreover, different scenarios have been defined to better challenge the fusion methods, with various contexts and contents providing a wide variety of meaningful data for fusion purposes, including non-planar scenes, where objects appear on different depth planes. However, there are several difficulties in creating datasets for research in infrared/visible-light image fusion. Camera calibration, registration, and synchronization can be listed as important steps of this task. In particular, image registration between imagery from sensors of different spectral bands imposes additional difficulties, as it is very challenging to solve the correspondence problem between such images. Motivated by these challenges, this work introduces a novel spatiotemporal video registration method capable of generating registered and temporally aligned infrared/visible-light video sequences. The proposed workflow improves the registration accuracy when compared to the state-of-the art. By applying the proposed methodology to the recorded database we have generated the visible-light and infrared video database for image fusion, a publicly available database to be used by the research community to test and benchmark fusion schemes. |
| abstractGer |
Abstract In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, there is a particular shortage of databases with registered and synchronized videos from the infrared and visible-light spectra suitable for image/video fusion research. To address this need we recorded an image/video fusion database using infrared and visible-light cameras under varying illumination conditions. Moreover, different scenarios have been defined to better challenge the fusion methods, with various contexts and contents providing a wide variety of meaningful data for fusion purposes, including non-planar scenes, where objects appear on different depth planes. However, there are several difficulties in creating datasets for research in infrared/visible-light image fusion. Camera calibration, registration, and synchronization can be listed as important steps of this task. In particular, image registration between imagery from sensors of different spectral bands imposes additional difficulties, as it is very challenging to solve the correspondence problem between such images. Motivated by these challenges, this work introduces a novel spatiotemporal video registration method capable of generating registered and temporally aligned infrared/visible-light video sequences. The proposed workflow improves the registration accuracy when compared to the state-of-the art. By applying the proposed methodology to the recorded database we have generated the visible-light and infrared video database for image fusion, a publicly available database to be used by the research community to test and benchmark fusion schemes. |
| abstract_unstemmed |
Abstract In general, the fusion of visible-light and infrared images produces a composite representation where both data are pictured in a single image. The successful development of image/video fusion algorithms relies on realistic infrared/visible-light datasets. To the best of our knowledge, there is a particular shortage of databases with registered and synchronized videos from the infrared and visible-light spectra suitable for image/video fusion research. To address this need we recorded an image/video fusion database using infrared and visible-light cameras under varying illumination conditions. Moreover, different scenarios have been defined to better challenge the fusion methods, with various contexts and contents providing a wide variety of meaningful data for fusion purposes, including non-planar scenes, where objects appear on different depth planes. However, there are several difficulties in creating datasets for research in infrared/visible-light image fusion. Camera calibration, registration, and synchronization can be listed as important steps of this task. In particular, image registration between imagery from sensors of different spectral bands imposes additional difficulties, as it is very challenging to solve the correspondence problem between such images. Motivated by these challenges, this work introduces a novel spatiotemporal video registration method capable of generating registered and temporally aligned infrared/visible-light video sequences. The proposed workflow improves the registration accuracy when compared to the state-of-the art. By applying the proposed methodology to the recorded database we have generated the visible-light and infrared video database for image fusion, a publicly available database to be used by the research community to test and benchmark fusion schemes. |
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| container_issue |
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| title_short |
A visible-light and infrared video database for performance evaluation of video/image fusion methods |
| url |
https://dx.doi.org/10.1007/s11045-017-0548-y |
| remote_bool |
true |
| author2 |
Pagliari, Carla L. da Silva, Eduardo A. B. Gois, Jonathan N. Neves, Sergio R. |
| author2Str |
Pagliari, Carla L. da Silva, Eduardo A. B. Gois, Jonathan N. Neves, Sergio R. |
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| doi_str |
10.1007/s11045-017-0548-y |
| up_date |
2024-07-03T20:28:32.181Z |
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| score |
7.4009523 |