A novel robust method for outlier detection in structure and motion recovery for affine cameras is presented. It is an extension of the well-known Tomasi-Kanade factorization technique (C. Tomasi T. Kanade, 1992) designed to handle outliers. It can also be seen as an importation of the LMedS technique or RANSAC into the factorization framework. Based on the computation of distances between subspaces, it relates closely with the subspace-based factorization methods for the perspective case presented by G. Sparr (1996) and others and the subspace-based-factorization for affine cameras with missing data by D. Jacobs (1997). Key features of the method presented are its ability to compare different subspaces and the complete automation of the detection and elimination of outliers. Its performance and effectiveness are demonstrated by experiments involving simulated and real video sequences.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Outlier detection in video sequences under affine projection


    Contributors:
    Huynh, D.Q. (author) / Heyden, A. (author)


    Publication date :

    2001-01-01


    Size :

    951586 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Outlier Detection in Video Sequences under Affine Projection

    Huynh, D. Q. / Heyden, A. / IEEE | British Library Conference Proceedings | 2001


    Outlier Correction in Image Sequences for the Affine Camera

    Huynh, D. / Hartley, R. / Heyden, A. et al. | British Library Conference Proceedings | 2003


    Outlier correction in image sequences for the affine camera

    Huynh, / Hartley, / Heyden, | IEEE | 2003


    Error Coded Affine Projection Like Algorithm

    Rodriguez, Alejandro / Sanchez, Juan C. / Avalos, Juan G. | IEEE | 2015


    ST TD outlier detection

    Sun, Dihua / Zhao, Hongzhuan / Yue, Hang et al. | IET | 2017

    Free access