A novel method for two-dimensional pattern recognition and feature extraction, applicable to microprocessor-based vision systems, is presented which employs fractal geometric analysis. Fractal contour transformation and transform correlation techniques are discussed in relation to their effectiveness in classifying rotationally deformed images over a wide resolution range. vractal geometric analysis exhibits several attributes: 1. position-, size-, and rotation-invariance is preserved in the absence of image coordinate transformation, 2. invariance to out-of-plane rotation is exhibited over the range ±60° of broadside, and 3. out-of-plane rotation can be computed from imagery and quantified in terms of the fractal dimension. This work is supported by experimental verification of a ship silhouette recognition algorithm. Results are presented in terms of recognition ratio and computational load.


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    Title :

    Shift-Invariant Recognition Of Rotationatly Deformed Ship Silhouettes At Multiple Resolution Scales


    Contributors:

    Conference:

    Hybrid Image Processing ; 1986 ; Orlando,United States


    Published in:

    Publication date :

    1986-10-15





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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