@InProceedings{8081320,
  author = {A. Gnutti and F. Guerrini and R. Leonardi},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {A normalized mirrored correlation measure for data symmetry detection},
  year = {2017},
  pages = {813-817},
  abstract = {Symmetry detection algorithms are enjoying a renovated interest in the scientific community, fueled by recent advancements in computer vision and computer graphics applications. This paper is inspired by recent efforts in building a symmetric object detection system in natural images. In particular, it is first shown how correlation can be a core operator that allows finding local reflection symmetry points in 1-D sequences that are optimal in an energetic sense. Then, the importance of 2-D correlation in natural images to correctly align the symmetric object axis is demonstrated. Using the correlation as described is crucial in boosting the performance of the system, as proven by the results on a standard dataset.},
  keywords = {correlation methods;image matching;object detection;symmetry;symmetric object detection system;natural images;local reflection symmetry points;symmetric object axis;normalized mirrored correlation measure;data symmetry detection;computer vision;computer graphics applications;2D correlation;1D sequences;Correlation;Object detection;Feature extraction;Convolution;Europe;Normalized Cross-Correlation;Reflection Symmetry Detection;Content-Based Analysis;Feature Extraction Methods},
  doi = {10.23919/EUSIPCO.2017.8081320},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347823.pdf},
}

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