Commercial databases containing images of tire tread patterns are currently used by product designers, forensic specialists and product application personnel to identify whether a given tread pattern matches an existing tire. Currently, this pattern matching process is almost entirely manual, requiring visual searches of extensive libraries of tire tread patterns. This work explores a first step toward automating this pattern matching process by building on feature analysis techniques from computer vision and image processing to develop a new method for extracting and classifying features from tire tread patterns and automatically locating candidate matches from a database of existing tread pattern images. The method begins with a selection of tire tread images obtained from multiple sources which are pre-processed and normalized using tow-dimensional Fast Fourier Transforms (2D-FFT). The results of this reprocessing are feature-rich images that are further analyzed using feature extraction algorithms drawn from research in computer vision. A new, feature extraction algorithm is developed based on the geometry of the 2D-FFT images of the tire. The resulting FFT-based analysis allows independent classification of the tire images along two dimensions, specifically by separating 'rib' and 'lug' features of the tread pattern. Dimensionality of (0,0) indicates a smooth treaded tire with no pattern; dimensionality of (1,0) and (0,1) are purely rib and lug tires; and dimensionality of (1,1) is an all-season pattern. This analysis technique allows a candidate tire to be classified according to the features of its tread pattern, and other tires with similar features and tread pattern classifications can be automatically retrieved from the database.


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    Title :

    Pattern recognition for classification and matching of car tires


    Additional title:

    Mustererkennung zur Klassifizierung und zum Abgleich von Reifen


    Contributors:
    Colbry, D. (author) / Cherba, D. (author) / Luchini, J. (author)


    Publication date :

    2005


    Size :

    16 Seiten, 8 Bilder, 13 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English





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