We deal with the problem of partially observed objects. These objects are defined by sets of points and their shape variations are represented by a statistical model. We present two models: a linear model based on PCA and a non-linear model based on KPCA (kernel PCA). The present work attempts to localize non visible parts of an object from visible parts and from the model, explicitly. using the variability represented by the model. Both are applied to the cephalometric problem with good results.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Partially observed objects localization with PCA and KPCA models


    Beteiligte:
    Romaniuk, B. (Autor:in) / Guilloux, V. (Autor:in) / Desvignes, M. (Autor:in) / Deshayes, M.J. (Autor:in)


    Erscheinungsdatum :

    01.01.2004


    Format / Umfang :

    297845 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    DBSCAN Based Parameter Optimization of KPCA for Fault Diagnosis

    Shaojun, Liang / Xing, Zheng / Lipeng, Xie et al. | TIBKAT | 2022




    Application of KPCA and PNN for Robust Speaker Identification

    Ren, Xue-Hui / Zhang, Ya-Fen / Xing, Yu-Juan et al. | IEEE | 2008


    DBSCAN Based Parameter Optimization of KPCA for Fault Diagnosis

    Shaojun, Liang / Xing, Zheng / Lipeng, Xie et al. | Springer Verlag | 2021