In robotic applications often highly specific objects need to be recognized, e.g. industrial parts, for which methods can’t rely on the online availability of large labeled training data sets or pre-trained mod- els. This is especially valid for depth data, thus making it challenging for deep learning (DL) approaches. Therefore, this work analyzes the per- formance of various traditional (global or part-based) and DL features on a restricted depth data set, depending on the tasks complexity. While the sample size is small, we can conclude that pre-trained DL descriptors are the most descriptive but not by a statistically significant margin and therefore part-based descriptors are still a viable option for small but difficult 3D data sets.


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    Titel :

    Applicability of Deep Learned vs Traditional Features for Depth Based Classification


    Beteiligte:
    Bracci, F (Autor:in) / Li, M (Autor:in) / Marton, Zoltan-Csaba (Autor:in) / Kossyk, Ingo (Autor:in)

    Kongress:



    Erscheinungsdatum :

    2018-07-01



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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