In this paper we have presented a classification scheme using the geometric moments as features. The hybrid system generates the features optically and performs the classification digitally. The system provides invariance to distortion and allows corrections for nonuniformities and nonlinearities in the optical system. For the moment feature set, we can describe analytically the probability density function set, we can describe analytically the probability density function for the moments and thereby derive an optimum classification scheme, eliminating the need for a training set. We have tested our classification system through digital simulation on real world ship images using reference feature vectors obtained only from models. The performance obtained was excellent and the preprocessing required only simple histogram and thresholding operations for image segmentation. These operations included producing row-projection histograms (from which the image regions were located) and statistical estimates of each region.


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    Title :

    Distortion invariant recognition using a moment feature space


    Additional title:

    Verbesserungsinvariante Zielerkennung unter Verwendung von einem momentanen Ortsmerkmal


    Contributors:
    Cheatham, L. (author) / Casasent, D. (author) / Fetterly, D. (author)


    Publication date :

    1983


    Size :

    4 Seiten, 7 Bilder, 5 Quellen


    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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