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Visible-infrared fusion schemes for road obstacle classification.

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Title: Visible-infrared fusion schemes for road obstacle classification.
Authors: Apatean, Anca1 anca.apatean@com.utcluj.ro, Rogozan, Alexandrina2, Bensrhair, Abdelaziz2
Source: Transportation Research Part C: Emerging Technologies. Oct2013, Vol. 35, p180-192. 13p.
Subjects: Infrared imaging, Algorithms, Dynamics, Flicker fusion, Kernel (Mathematics), Thermography
Abstract: Highlights: [•] We aimed VIS-IR probabilistic fusion schemes at features, kernels and matching scores. [•] We use an adapted static or dynamic weighting of VIS and IR modalities. [•] The problem of a multi-class road obstacle classification was approached using SVMs. [•] Different families of features were combined. [•] Different features selection algorithms were tested. [ABSTRACT FROM AUTHOR]
Copyright of Transportation Research Part C: Emerging Technologies is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="JN" term="%22Transportation+Research+Part+C%3A+Emerging+Technologies%22">Transportation Research Part C: Emerging Technologies</searchLink>. Oct2013, Vol. 35, p180-192. 13p.
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  Data: Highlights: [•] We aimed VIS-IR probabilistic fusion schemes at features, kernels and matching scores. [•] We use an adapted static or dynamic weighting of VIS and IR modalities. [•] The problem of a multi-class road obstacle classification was approached using SVMs. [•] Different families of features were combined. [•] Different features selection algorithms were tested. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Transportation Research Part C: Emerging Technologies is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.trc.2013.07.003
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      – Code: eng
        Text: English
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        PageCount: 13
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      – SubjectFull: Infrared imaging
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Dynamics
        Type: general
      – SubjectFull: Flicker fusion
        Type: general
      – SubjectFull: Kernel (Mathematics)
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      – SubjectFull: Thermography
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      – TitleFull: Visible-infrared fusion schemes for road obstacle classification.
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            NameFull: Apatean, Anca
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            NameFull: Rogozan, Alexandrina
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              M: 10
              Text: Oct2013
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              Y: 2013
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              Value: 35
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            – TitleFull: Transportation Research Part C: Emerging Technologies
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