While the view of constructive and hierarchical vision prevails, the issues of cooperation and competition among individual modules become crucial. These issues are directly related to one of the most important aspects in computer vision research: integration. A major source of difficulty in developing a consistent and systematic integration formalism is the heterogeneity existing in modules, in information, and in knowledge. The author exploits, using the central theme of grouping, the homogeneous characteristics in vision problem solving and proposes a general framework, called hierarchical token grouping, that facilitates vision problem solving by providing a consistent and systematic environment for integrating modules, cues, and knowledge, all in a globally coherent mechanism.<>


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    Title :

    A general framework for machine vision: hierarchical token grouping


    Contributors:
    Qian Huang (author)


    Publication date :

    1994-01-01


    Size :

    401167 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A General Framework for Machine Vision: Hierarchical Token Grouping

    Huang, Q. / IEEE; Hong Kong Chapter of Signal Processing | British Library Conference Proceedings | 1994



    A General Vision Problem Solving Architecture: Hierarchical Token Grouping

    Huang, Q. / IEEE; Signal Processing Society | British Library Conference Proceedings | 1994


    Formalism for integrating machine vision systems: hierarchical token grouping [2347-13]

    Huang, Q. / SPIE | British Library Conference Proceedings | 1994