We address the problem of recognizing 2-D shapes in images via multi-class classifications. Our approach has three key elements. First, a signed distance transform is introduced to represent a shape more informatively. Second, a filter bank is generated such that its filters can capture multiple-scale local and global features between two shapes of different classes. We then apply boosting to combine useful filters to construct discriminant classifiers. Third, in implementing our system, a new classification architecture is developed to accomplish multi-class recognition. To examine the claimed efficiencies, we consider an example of document recognition by pinpointing the strengths of our method through experimental results and comparisons.


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    Title :

    Shape recognition using fast boosted filtering


    Contributors:
    Yen-Yu Lin, (author) / Tyng-Luh Liu, (author)


    Publication date :

    2005-01-01


    Size :

    981493 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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