Cascaded classifiers are frequently used for object detection tasks. The base classifiers in a cascaded classifier can be of any type but the types of features used by these classifiers have a strong impact on the performance of the cascaded classifier. In this paper we present a method for the extraction of multiple features inside a rectangular region relying on integral images. The rectangular regions are chosen in a classifier training process and are sensitive to the geometrical properties of an object while the features extracted inside the regions are sensitive to structural characteristics. In combination with a cascaded classifier this results in a large variety of features sensitive to the geometrical as well as structural properties. Our experimental evaluation shows that a combination of both tensor and intensity based features with a specific normalization method yields the best performance of the examined approaches.


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    Titel :

    Resource optimized cascaded perceptron classifiers using structure tensor features for us speed limit detection


    Weitere Titelangaben:

    Möglichkeitsoptimierter, kaskadierter Perzeptron-Klassifizierer mit Hilfe von Tensorstruktur-Merkmalen für den Einsatz der US-Geschwindigkeitsbegrenzung


    Beteiligte:


    Erscheinungsdatum :

    2010


    Format / Umfang :

    6 Seiten, 4 Bilder, 3 Tabellen, 20 Quellen


    Anmerkungen:

    (nicht paginiert)


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Datenträger


    Sprache :

    Englisch





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