A new method for generating X-Y separable, steerable, scalable approximations of filter kernels is proposed which is based on a generalization of the singular value decomposition (SVD) to three dimensions. This "pseudo-SVD" improves upon a previous scheme due to Perona (1992) in that it reduces convolution time and storage requirements. An adaptation of the pseudo-SVD is proposed to generate steerable and scalable kernels which are suitable for use with a Laplacian pyramid. The properties of this method are illustrated experimentally in generating steerable and scalable approximations to an early vision edge-detection kernel.<>


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    Title :

    X-Y separable pyramid steerable scalable kernels


    Contributors:
    Shy (author) / Perona (author)


    Publication date :

    1994-01-01


    Size :

    786360 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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