This paper presents an effective use of local descriptors for object or scene recognition and indexing. This approach is in keeping with model-based recognition systems and consists of an extension of a standard point-to-point matching between two images. Aiming at this, we address the use of Full-Zernike moments as a reliable local characterization of the image signal. A fundamental characteristic of the used descriptors is then their ability to "absorb" a given set of potential image modifications. Their design calls principally for the theory of invariants. A built-in invariance to similarities allows one to manage narrow bounded perspective transformations. Moreover we provide a study of the substantial and costless contribution of the use of color information. In order to achieve photometric invariance, different types of normalization are evaluated through a model-based object recognition task.


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    Title :

    A local color descriptor for efficient scene-object recognition


    Contributors:
    Bigorgne, E. (author) / Achard, C. (author) / Devars, J. (author)


    Publication date :

    2001-01-01


    Size :

    592594 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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