The eight-point algorithm of Hartley occupies an important place in computer vision, notably as a means of providing an initial value of the fundamental matrix for use in iterative estimation methods. In this paper, a novel explanation is given for the improvement in performance of the eight-point algorithm that results from using normalised data. A first step is singling out a cost function that the normalised algorithm acts to minimise. The cost function is then shown to be statistically better founded than the cost function associated with the non-normalised algorithm. This augments the original argument that improved performance is due to the better conditioning of a pivotal matrix. Experimental results are given that support the adopted approach. This work continues a wider effort to place a variety of estimation techniques within a coherent framework.


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    Title :

    A statistical rationalisation of Hartley's normalised eight-point algorithm


    Contributors:


    Publication date :

    2003-01-01


    Size :

    351856 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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