We present a self-organizing framework called the SHOSLIF-M for learning and recognizing spatiotemporal events (or patterns) from intensity image sequences. The proposed framework consists of a multiclass, multivariate discriminant analysis to automatically select the most discriminating features (MDF), a space partition tree to achieve a logarithmic retrieval time complexity for a database of n items, and a general interpolation scheme to do view inference and generalization in the MDF space based on a small number of training samples. The system is tested to recognize 28 different hand signs. The experimental results show that the learned system can achieve a 96% recognition rate for test sequences that have not been used in the training phase.<>


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    Title :

    Learning-based hand sign recognition using SHOSLIF-M


    Contributors:
    Yuntao Cui (author) / Swets, D.L. (author) / Weng, J.J. (author)


    Publication date :

    1995-01-01


    Size :

    587686 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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