Optic Flow in Harmony
Abstract Most variational optic flow approaches just consist of three constituents: a data term, a smoothness term and a smoothness weight. In this paper, we present an approach that harmonises these three components. We start by developing an advanced data term that is robust under outliers and var...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zimmer, Henning [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2011 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of computer vision - Springer US, 1987, 93(2011), 3 vom: 26. Jan., Seite 368-388 |
|---|---|
| Übergeordnetes Werk: |
volume:93 ; year:2011 ; number:3 ; day:26 ; month:01 ; pages:368-388 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11263-011-0422-6 |
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| 520 | |a Abstract Most variational optic flow approaches just consist of three constituents: a data term, a smoothness term and a smoothness weight. In this paper, we present an approach that harmonises these three components. We start by developing an advanced data term that is robust under outliers and varying illumination conditions. This is achieved by using constraint normalisation, and an HSV colour representation with higher order constancy assumptions and a separate robust penalisation. Our novel anisotropic smoothness is designed to work complementary to the data term. To this end, it incorporates directional information from the data constraints to enable a filling-in of information solely in the direction where the data term gives no information, yielding an optimal complementary smoothing behaviour. This strategy is applied in the spatial as well as in the spatio-temporal domain. Finally, we propose a simple method for automatically determining the optimal smoothness weight. This method bases on a novel concept that we call “optimal prediction principle” (OPP). It states that the flow field obtained with the optimal smoothness weight allows for the best prediction of the next frames in the image sequence. The benefits of our “optic flow in harmony” (OFH) approach are demonstrated by an extensive experimental validation and by a competitive performance at the widely used Middlebury optic flow benchmark. | ||
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10.1007/s11263-011-0422-6 doi (DE-627)OLC2057745546 (DE-He213)s11263-011-0422-6-p DE-627 ger DE-627 rakwb eng 004 VZ Zimmer, Henning verfasserin aut Optic Flow in Harmony 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract Most variational optic flow approaches just consist of three constituents: a data term, a smoothness term and a smoothness weight. In this paper, we present an approach that harmonises these three components. We start by developing an advanced data term that is robust under outliers and varying illumination conditions. This is achieved by using constraint normalisation, and an HSV colour representation with higher order constancy assumptions and a separate robust penalisation. Our novel anisotropic smoothness is designed to work complementary to the data term. To this end, it incorporates directional information from the data constraints to enable a filling-in of information solely in the direction where the data term gives no information, yielding an optimal complementary smoothing behaviour. This strategy is applied in the spatial as well as in the spatio-temporal domain. Finally, we propose a simple method for automatically determining the optimal smoothness weight. This method bases on a novel concept that we call “optimal prediction principle” (OPP). It states that the flow field obtained with the optimal smoothness weight allows for the best prediction of the next frames in the image sequence. The benefits of our “optic flow in harmony” (OFH) approach are demonstrated by an extensive experimental validation and by a competitive performance at the widely used Middlebury optic flow benchmark. Optic flow Variational methods Anisotropic smoothing Robust penalisation Motion tensor Parameter selection Bruhn, Andrés aut Weickert, Joachim aut Enthalten in International journal of computer vision Springer US, 1987 93(2011), 3 vom: 26. Jan., Seite 368-388 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:93 year:2011 number:3 day:26 month:01 pages:368-388 https://doi.org/10.1007/s11263-011-0422-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 93 2011 3 26 01 368-388 |
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10.1007/s11263-011-0422-6 doi (DE-627)OLC2057745546 (DE-He213)s11263-011-0422-6-p DE-627 ger DE-627 rakwb eng 004 VZ Zimmer, Henning verfasserin aut Optic Flow in Harmony 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract Most variational optic flow approaches just consist of three constituents: a data term, a smoothness term and a smoothness weight. In this paper, we present an approach that harmonises these three components. We start by developing an advanced data term that is robust under outliers and varying illumination conditions. This is achieved by using constraint normalisation, and an HSV colour representation with higher order constancy assumptions and a separate robust penalisation. Our novel anisotropic smoothness is designed to work complementary to the data term. To this end, it incorporates directional information from the data constraints to enable a filling-in of information solely in the direction where the data term gives no information, yielding an optimal complementary smoothing behaviour. This strategy is applied in the spatial as well as in the spatio-temporal domain. Finally, we propose a simple method for automatically determining the optimal smoothness weight. This method bases on a novel concept that we call “optimal prediction principle” (OPP). It states that the flow field obtained with the optimal smoothness weight allows for the best prediction of the next frames in the image sequence. The benefits of our “optic flow in harmony” (OFH) approach are demonstrated by an extensive experimental validation and by a competitive performance at the widely used Middlebury optic flow benchmark. Optic flow Variational methods Anisotropic smoothing Robust penalisation Motion tensor Parameter selection Bruhn, Andrés aut Weickert, Joachim aut Enthalten in International journal of computer vision Springer US, 1987 93(2011), 3 vom: 26. Jan., Seite 368-388 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:93 year:2011 number:3 day:26 month:01 pages:368-388 https://doi.org/10.1007/s11263-011-0422-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 93 2011 3 26 01 368-388 |
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10.1007/s11263-011-0422-6 doi (DE-627)OLC2057745546 (DE-He213)s11263-011-0422-6-p DE-627 ger DE-627 rakwb eng 004 VZ Zimmer, Henning verfasserin aut Optic Flow in Harmony 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract Most variational optic flow approaches just consist of three constituents: a data term, a smoothness term and a smoothness weight. In this paper, we present an approach that harmonises these three components. We start by developing an advanced data term that is robust under outliers and varying illumination conditions. This is achieved by using constraint normalisation, and an HSV colour representation with higher order constancy assumptions and a separate robust penalisation. Our novel anisotropic smoothness is designed to work complementary to the data term. To this end, it incorporates directional information from the data constraints to enable a filling-in of information solely in the direction where the data term gives no information, yielding an optimal complementary smoothing behaviour. This strategy is applied in the spatial as well as in the spatio-temporal domain. Finally, we propose a simple method for automatically determining the optimal smoothness weight. This method bases on a novel concept that we call “optimal prediction principle” (OPP). It states that the flow field obtained with the optimal smoothness weight allows for the best prediction of the next frames in the image sequence. The benefits of our “optic flow in harmony” (OFH) approach are demonstrated by an extensive experimental validation and by a competitive performance at the widely used Middlebury optic flow benchmark. Optic flow Variational methods Anisotropic smoothing Robust penalisation Motion tensor Parameter selection Bruhn, Andrés aut Weickert, Joachim aut Enthalten in International journal of computer vision Springer US, 1987 93(2011), 3 vom: 26. Jan., Seite 368-388 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:93 year:2011 number:3 day:26 month:01 pages:368-388 https://doi.org/10.1007/s11263-011-0422-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 93 2011 3 26 01 368-388 |
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10.1007/s11263-011-0422-6 doi (DE-627)OLC2057745546 (DE-He213)s11263-011-0422-6-p DE-627 ger DE-627 rakwb eng 004 VZ Zimmer, Henning verfasserin aut Optic Flow in Harmony 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract Most variational optic flow approaches just consist of three constituents: a data term, a smoothness term and a smoothness weight. In this paper, we present an approach that harmonises these three components. We start by developing an advanced data term that is robust under outliers and varying illumination conditions. This is achieved by using constraint normalisation, and an HSV colour representation with higher order constancy assumptions and a separate robust penalisation. Our novel anisotropic smoothness is designed to work complementary to the data term. To this end, it incorporates directional information from the data constraints to enable a filling-in of information solely in the direction where the data term gives no information, yielding an optimal complementary smoothing behaviour. This strategy is applied in the spatial as well as in the spatio-temporal domain. Finally, we propose a simple method for automatically determining the optimal smoothness weight. This method bases on a novel concept that we call “optimal prediction principle” (OPP). It states that the flow field obtained with the optimal smoothness weight allows for the best prediction of the next frames in the image sequence. The benefits of our “optic flow in harmony” (OFH) approach are demonstrated by an extensive experimental validation and by a competitive performance at the widely used Middlebury optic flow benchmark. Optic flow Variational methods Anisotropic smoothing Robust penalisation Motion tensor Parameter selection Bruhn, Andrés aut Weickert, Joachim aut Enthalten in International journal of computer vision Springer US, 1987 93(2011), 3 vom: 26. Jan., Seite 368-388 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:93 year:2011 number:3 day:26 month:01 pages:368-388 https://doi.org/10.1007/s11263-011-0422-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 93 2011 3 26 01 368-388 |
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10.1007/s11263-011-0422-6 doi (DE-627)OLC2057745546 (DE-He213)s11263-011-0422-6-p DE-627 ger DE-627 rakwb eng 004 VZ Zimmer, Henning verfasserin aut Optic Flow in Harmony 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2011 Abstract Most variational optic flow approaches just consist of three constituents: a data term, a smoothness term and a smoothness weight. In this paper, we present an approach that harmonises these three components. We start by developing an advanced data term that is robust under outliers and varying illumination conditions. This is achieved by using constraint normalisation, and an HSV colour representation with higher order constancy assumptions and a separate robust penalisation. Our novel anisotropic smoothness is designed to work complementary to the data term. To this end, it incorporates directional information from the data constraints to enable a filling-in of information solely in the direction where the data term gives no information, yielding an optimal complementary smoothing behaviour. This strategy is applied in the spatial as well as in the spatio-temporal domain. Finally, we propose a simple method for automatically determining the optimal smoothness weight. This method bases on a novel concept that we call “optimal prediction principle” (OPP). It states that the flow field obtained with the optimal smoothness weight allows for the best prediction of the next frames in the image sequence. The benefits of our “optic flow in harmony” (OFH) approach are demonstrated by an extensive experimental validation and by a competitive performance at the widely used Middlebury optic flow benchmark. Optic flow Variational methods Anisotropic smoothing Robust penalisation Motion tensor Parameter selection Bruhn, Andrés aut Weickert, Joachim aut Enthalten in International journal of computer vision Springer US, 1987 93(2011), 3 vom: 26. Jan., Seite 368-388 (DE-627)129354252 (DE-600)155895-X (DE-576)018081428 0920-5691 nnns volume:93 year:2011 number:3 day:26 month:01 pages:368-388 https://doi.org/10.1007/s11263-011-0422-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_70 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2244 GBV_ILN_4046 GBV_ILN_4700 AR 93 2011 3 26 01 368-388 |
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optic flow in harmony |
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Optic Flow in Harmony |
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Abstract Most variational optic flow approaches just consist of three constituents: a data term, a smoothness term and a smoothness weight. In this paper, we present an approach that harmonises these three components. We start by developing an advanced data term that is robust under outliers and varying illumination conditions. This is achieved by using constraint normalisation, and an HSV colour representation with higher order constancy assumptions and a separate robust penalisation. Our novel anisotropic smoothness is designed to work complementary to the data term. To this end, it incorporates directional information from the data constraints to enable a filling-in of information solely in the direction where the data term gives no information, yielding an optimal complementary smoothing behaviour. This strategy is applied in the spatial as well as in the spatio-temporal domain. Finally, we propose a simple method for automatically determining the optimal smoothness weight. This method bases on a novel concept that we call “optimal prediction principle” (OPP). It states that the flow field obtained with the optimal smoothness weight allows for the best prediction of the next frames in the image sequence. The benefits of our “optic flow in harmony” (OFH) approach are demonstrated by an extensive experimental validation and by a competitive performance at the widely used Middlebury optic flow benchmark. © Springer Science+Business Media, LLC 2011 |
| abstractGer |
Abstract Most variational optic flow approaches just consist of three constituents: a data term, a smoothness term and a smoothness weight. In this paper, we present an approach that harmonises these three components. We start by developing an advanced data term that is robust under outliers and varying illumination conditions. This is achieved by using constraint normalisation, and an HSV colour representation with higher order constancy assumptions and a separate robust penalisation. Our novel anisotropic smoothness is designed to work complementary to the data term. To this end, it incorporates directional information from the data constraints to enable a filling-in of information solely in the direction where the data term gives no information, yielding an optimal complementary smoothing behaviour. This strategy is applied in the spatial as well as in the spatio-temporal domain. Finally, we propose a simple method for automatically determining the optimal smoothness weight. This method bases on a novel concept that we call “optimal prediction principle” (OPP). It states that the flow field obtained with the optimal smoothness weight allows for the best prediction of the next frames in the image sequence. The benefits of our “optic flow in harmony” (OFH) approach are demonstrated by an extensive experimental validation and by a competitive performance at the widely used Middlebury optic flow benchmark. © Springer Science+Business Media, LLC 2011 |
| abstract_unstemmed |
Abstract Most variational optic flow approaches just consist of three constituents: a data term, a smoothness term and a smoothness weight. In this paper, we present an approach that harmonises these three components. We start by developing an advanced data term that is robust under outliers and varying illumination conditions. This is achieved by using constraint normalisation, and an HSV colour representation with higher order constancy assumptions and a separate robust penalisation. Our novel anisotropic smoothness is designed to work complementary to the data term. To this end, it incorporates directional information from the data constraints to enable a filling-in of information solely in the direction where the data term gives no information, yielding an optimal complementary smoothing behaviour. This strategy is applied in the spatial as well as in the spatio-temporal domain. Finally, we propose a simple method for automatically determining the optimal smoothness weight. This method bases on a novel concept that we call “optimal prediction principle” (OPP). It states that the flow field obtained with the optimal smoothness weight allows for the best prediction of the next frames in the image sequence. The benefits of our “optic flow in harmony” (OFH) approach are demonstrated by an extensive experimental validation and by a competitive performance at the widely used Middlebury optic flow benchmark. © Springer Science+Business Media, LLC 2011 |
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