Designing a pattern classifier remains a difficult problem, especially in the presence of noise and other degradations. Combination of multiple classifiers appears to be a good way of retaining the strengths of different classifiers while avoiding their weaknesses. Different combination schemes were proposed in the literature. As a special case of combining multiple classifiers, we consider combining correlators. Correlators are attractive for use in automatic target recognition (ATR) systems. Many correlation filter designs have been developed, each with its own features. Some filter designs maximize noise tolerance but do not provide sharp peaks. On the other hand, some correlation filters yield sharp correlation peaks but are overly sensitive to input noise. In this research effort, we explore the use of an artificial neural network as a tool for combining correlators. Results of this implementation show improvements and indicate that combination of multiple correlators can potentially improve the classification performance.


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    Title :

    Combining multiple correlators using neural networks


    Contributors:


    Publication date :

    1997


    Size :

    6 Seiten, 8 Quellen





    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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