Many studies have been made in the past for optimization using covariance matrices of feature points. We first describe how to compute the covariance matrix of a feature point from the gray levels by integrating existing methods. Then, we experimentally examine if thus computed covariance matrices really reflect the accuracy of the feature points. To test this, we do subpixel template matching and compute the homography and the fundamental matrix. Our conclusion is rather surprising, pointing out important elements often overlooked.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Do we really have to consider covariance matrices for image features?


    Contributors:
    Kanazawa, Y. (author) / Kanatani, K. (author)


    Publication date :

    2001-01-01


    Size :

    781676 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Do We Really Have to Consider Covariance Matrices for Image Features?

    Kanazawa, Y. / Kanatani, K. / IEEE | British Library Conference Proceedings | 2001


    Do we really have to consider data mining techniques for meteorological data

    Mazhar, Aneela / Ikram, Muhammad Touseef / Butt, Naveed Anwer et al. | IEEE | 2015



    A Multipurpose Consider Covariance Analysis for Square-Root Information Smoothers

    Hinks, J. / Psiaki, M. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2012