A fundamental problem in image analysis is the integration of information across scale to detect and classify objects. We have developed, within a machine learning framework, two classes of multiresolution models for integrating scale information for object detection and classification-a discriminative model called the hierarchical pyramid neural network and a generative model called a hierarchical image probability model. Using receiver operating characteristic analysis, we show that these models can significantly reduce the false positive rates for a well-established computer-aided diagnosis system.


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    Titel :

    Hierarchical, multi-resolution models for object recognition: applications to mammographic computer-aided diagnosis


    Beteiligte:
    Sajda, P. (Autor:in) / Spence, C. (Autor:in) / Parra, L. (Autor:in) / Nishikawa, R. (Autor:in)


    Erscheinungsdatum :

    01.01.2000


    Format / Umfang :

    718086 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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