@InProceedings{7760640,
  author = {L. Denis and A. Ferrari and D. Mary and L. Mugnier and E. Thiébaut},
  booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
  title = {Fast and robust detection of a known pattern in an image},
  year = {2016},
  pages = {2206-2210},
  abstract = {Many image processing applications require to detect a known pattern buried under noise. While maximum correlation can be implemented efficiently using fast Fourier transforms, detection criteria that are robust to the presence of outliers are typically slower by several orders of magnitude. We derive the general expression of a robust detection criterion based on the theory of locally optimal detectors. The expression of the criterion is attractive because it offers a fast implementation based on correlations. Application of this criterion to Cauchy likelihood gives good detection performance in the presence of outliers, as shown in our numerical experiments. Special attention is given to proper normalization of the criterion in order to account for truncation at the image borders and noise with a non-stationary dispersion.},
  keywords = {fast Fourier transforms;image processing;maximum likelihood detection;object detection;image processing applications;robust known pattern detection;maximum correlation;fast Fourier transform;Cauchy likelihood;nonstationary dispersion;image noise;image border;image truncation;Robustness;Detectors;Correlation;Mathematical model;Europe;Dispersion;robust detection;locally most powerful test (LMP);Cauchy distribution},
  doi = {10.1109/EUSIPCO.2016.7760640},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570256301.pdf},
}

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