Improving the discrimination of synthetic discriminant filters

  • S. K. Mayo
Classifiation Techniques
Part of the Lecture Notes in Computer Science book series (LNCS, volume 301)


Synthetic Discriminant Filters (SDF) are theoretically capable of distortion invariant multiclass recognition. However a serious practical limitation is the trade-off between invariance and specificity. Three techniques were investigated for improving the ability of the SDF to perform 2-D rotation invariant recognition of an industrial component from amongst similarly shaped objects. The first technique involved extracting features from the correlation image. A trade-off was found between the number and/or complexity of features which must be extracted and the number of training set images in the SDF. The second technique was edge enhancing the SDF and/or test images. Edge enhancing the test images gave greater discrimination improvement than did edge enhancing the SDF. The third method was phase only filtering. Phase only SDFs gave a dramatic improvement in discrimination between training set targets and clutter although rejection of non training set targets increases. Increasing the number of training set image remedied this.


Test Image Binary Image Correlation Image Correlation Peak Edge Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • S. K. Mayo
    • 1
  1. 1.GEC Research Limited, Hirst Research Centre East LaneWembleyUK

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