We introduce a novel set of features for robust object recognition. Each element of this set is a complex feature obtained by combining position- and scale-tolerant edge-detectors over neighboring positions and multiple orientations. Our system's architecture is motivated by a quantitative model of visual cortex. We show that our approach exhibits excellent recognition performance and outperforms several state-of-the-art systems on a variety of image datasets including many different object categories. We also demonstrate that our system is able to learn from very few examples. The performance of the approach constitutes a suggestive plausibility proof for a class of feedforward models of object recognition in cortex.


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    Title :

    Object recognition with features inspired by visual cortex


    Contributors:
    Serre, T. (author) / Wolf, L. (author) / Poggio, T. (author)


    Publication date :

    2005-01-01


    Size :

    387271 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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