Principal Component Analysis (PCA) has been widely used for the representation of shape, appearance and motion. One drawback of typical PCA methods is that they are least squares estimation techniques and hence fail to account for "outliers" which are common in realistic training sets. In computer vision applications, outliers typically occur within a sample (image) due to pixels that are corrupted by noise, alignment errors, or occlusion. We review previous approaches for making PCA robust to outliers and present a new method that uses an intra-sample outlier process to account for pixel outliers. We develop the theory of Robust Principal Component Analysis (RPCA) and describe a robust M-estimation algorithm for learning linear multi-variate representations of high dimensional data such as images. Quantitative comparisons with traditional PCA and previous robust algorithms illustrate the benefits of RPCA when outliers are present. Details of the algorithm are described and a software implementation is being made publically available.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Robust principal component analysis for computer vision


    Beteiligte:
    De la Torre, F. (Autor:in) / Black, M.J. (Autor:in)


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    1114673 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Robust Principal Component Analysis for Computer Vision

    De la Torre, F. / Black, M. / IEEE | British Library Conference Proceedings | 2001


    Iris recognition based on robust principal component analysis

    Karn, P. / He, X.H. / Yang, S. et al. | British Library Online Contents | 2014


    Robust Tensor Principal Component Analysis Based on F-norm

    Ge, Weimin / Li, Jinxiang / Wang, Xiaofeng | British Library Conference Proceedings | 2020


    Data Reconstruction Based on Robust Kernel Principal Component Analysis

    Huang, Y. | British Library Online Contents | 2010