Weighted DFT Based Blur Invariants for Pattern Recognition
Recognition of patterns in blurred images can be achieved without deblurring of the images by using image features that are invariant to blur. All known blur invariants are based either on image moments or Fourier phase. In this paper, we introduce a method that improves the results obtained by existing state of the art blur invariant Fourier domain features. In this method, the invariants are weighted according to their reliability, which is proportional to their estimated signal-to-noise ratio. Because the invariants are non-linear functions of the image data, we apply a linearization scheme to estimate their noise covariance matrix, which is used for computation of the weighted distance between the images in classification. We applied similar weighting scheme to blur and blur-translation invariant features in the Fourier domain. For illustration we did experiments also with other Fourier and spatial domain features with and without weighting. In the experiments, the classification accuracy of the Fourier domain invariants was increased by up to 20 % through the use of weighting.
KeywordsDiscrete Fourier Transform Point Spread Function Mahalanobis Distance Invariant Feature Fourier Domain
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