A higher-order statistical theory of matching models against images is developed. The basic idea is to take into account how much of an object can be seen in the image, and what parts of it are jointly present. It is shown that this additional information can improve the specificity (i.e., reduce the probability of false positive matches) of a recognition algorithm. Higher-order statistics are derived from a physical world model and the minimum description length principle. Statistical information is used in a top-down way for the evaluation (verification) of specific model and pose hypotheses.<>


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Higher-order statistics in object recognition


    Contributors:


    Publication date :

    1993-01-01


    Size :

    177492 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Using order statistics for object tracking

    Werner, M. / von Seelen, W. | IEEE | 1997


    On detection using filter banks and higher order statistics

    Sattar, F. / Salomonsson, G. | IEEE | 2000


    Towards Local Isotropy of Higher Order Statistics in Wakes

    Tang, Shunlin / Antonia, Robert A. / Djenidi, Lyazid et al. | Springer Verlag | 2016


    Detection of small target using recursive higher order statistics

    Hou, Wang / Sun, Hongyuan / Lei, Zhihui | SPIE | 2014