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    This was brought to you by:

    block this user Jose M Martinez-Otzeta Trusted member

    Research Fellow

    Fundacion Tekniker, 20600, Eibar, Spain

    Image Analysis and Automatic Surface Identification by a Bi-Level Multi-Classifier

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    Combining the predictions of a set of classifiers has shown to be an e#ective way of creating composite classifiers that are more accurate than any of the component classifiers; we have performed a research work consisting of the design, development and experimental use of a multi-classifier system for image analysis and surface classification of the di#erent segments that might appear on a given picture in order to help a Mobile Robot in its navigation task. The presented approach combines a number of component classifiers which are standard machine learning classification algorithms, using a second layer paradigm to obtain a better classification accuracy. Experimental results have been obtained using a datafile of cases that contains information about surfaces, extracted from images obtained by the robot. The classification problem consists of recognizing to which of the surfaces belongs a n n size subimage.

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    Title : Image Analysis and Automatic Surface Identification by a Bi-Level Multi-Classifier
    Abstract : Combining the predictions of a set of classifiers has shown to be an e#ective way of creating composite classifiers that are more accurate than any of the component classifiers; we have performed a research work consisting of the design, development and experimental use of a multi-classifier system for image analysis and surface classification of the di#erent segments that might appear on a given picture in order to help a Mobile Robot in its navigation task. The presented approach combines a number of component classifiers which are standard machine learning classification algorithms, using a second layer paradigm to obtain a better classification accuracy. Experimental results have been obtained using a datafile of cases that contains information about surfaces, extracted from images obtained by the robot. The classification problem consists of recognizing to which of the surfaces belongs a n n size subimage.
    Subject : unspecified
    Area : Computer Science
    Language : English
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    Url : http://www.sc.ehu.es/ccwrobot/publications/papers/otzeta05image.pdf
    Doi : 10.1.1.61.9233

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