Unsupervised Segmentation of Anomalies in Sequential Data, Images and Volumetric Data Using Multiscale Fourier Phase-Only Analysis

  • Fabian Bürger
  • Josef Pauli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)


This paper presents an unsupervised method to detect and segment anomalies and novel patterns in sequential data, images and volumetric data. The proposed Multiscale Phase-Only Transformation (MPHOT) addresses the case when no prior knowledge about the data or even its dimensionality is provided. It is based on the fusion of the Phase-Only Transform (PHOT) in scale space using only one adaptive sensitivity parameter. The PHOT uses the Discrete Fourier Transform (DFT) to remove all regularities while it detects small defects and pattern boundaries. The proposed multiscale extension allows the precise segmentation of large anomalies as well. We present experiments on synthetic and measured data in fields of time series analysis, image processing and volumetric data segmentation to show the universality of our approach.


Unsupervised Anomaly Detection Novelty Detection Texture Segmentation Volumetric Segmentation Data Mining 


  1. 1.
    Aiger, D., Talbot, H.: The phase only transform for unsupervised surface defect detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 295–302 (2010)Google Scholar
  2. 2.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Computing Surveys 41(3), 1–58 (2009)CrossRefGoogle Scholar
  3. 3.
    Markou, M., Singh, S.: Novelty detection: a review – part 1+2. Signal Processing 83(12), 2481–2521 (2003)zbMATHCrossRefGoogle Scholar
  4. 4.
    Xie, X.: A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques. Electronic Letters on Computer Vision and Image Analysis 7, 1–22 (2008)Google Scholar
  5. 5.
    Wei, L., Kumar, N., Lolla, V., Keogh, E.J., Lonardi, S., Ratanamahatana, C.: Assumption-free anomaly detection in time series. In: Proceedings of the 17th International Conference on Scientific and Statistical Database Management, SSDBM 2005, pp. 237–240. Lawrence Berkeley Laboratory (2005)Google Scholar
  6. 6.
    Keogh, E., Lin, J., Fu, A.: HOT SAX: efficiently finding the most unusual time series subsequence. In: Fifth IEEE International Conference on Data Mining, p. 8 (2005)Google Scholar
  7. 7.
    Davy, M., Godsill, S.: Detection of abrupt spectral changes using support vector machines an application to audio signal segmentation. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 1313–1316 (2002)Google Scholar
  8. 8.
    Wolfe, J.: Guided Search 2.0 A revised model of visual search. Psychonomic Bulletin & Review 1, 202–238 (1994)CrossRefGoogle Scholar
  9. 9.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  10. 10.
    Hou, X., Zhang, L.: Saliency Detection: A Spectral Residual Approach. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  11. 11.
    Wang, Z., Li, B.: A two-stage approach to saliency detection in images. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 965–968 (2008)Google Scholar
  12. 12.
    Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Transactions on Image Processing 19(1), 185–198 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Oppenheim, A., Lim, J.: The importance of phase in signals. Proceedings of the IEEE 69(5), 529–541 (1981)CrossRefGoogle Scholar
  14. 14.
    Tolimieri, R., An, M., Lu, C.: Mathematics of multidimensional Fourier transform algorithms, vol. 2. Springer (1997)Google Scholar
  15. 15.
    Hewitt, E., Hewitt, R.: The Gibbs-Wilbraham phenomenon: An episode in fourier analysis. Archive for History of Exact Sciences 21, 129–160 (1979)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Brodatz, P.: A Photographic Album for Artists and Designers. Dover Publications (1966)Google Scholar
  17. 17.
    Acharya, R., Laurette, R.: Mathematical morphology for 3-D image analysis. In: International Conference on Acoustics, Speech, and Signal Processing, pp. 952–955 (1988)Google Scholar
  18. 18.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Van Rijsbergen, C.J.: Information retrieval. Butterworth-Heinemann (1979)Google Scholar
  20. 20.
    Quiroga, R.Q., Blanco, S., Rosso, O., Garcia, H., Rabinowicz, A.: Searching for hidden information with Gabor Transform in generalized tonic-clonic seizures. Electroencephalography and Clinical Neurophysiology 103(4), 434–439 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fabian Bürger
    • 1
  • Josef Pauli
    • 1
  1. 1.Intelligent Systems Group, Faculty of EngineeringUniversity of Duisburg-EssenGermany

Personalised recommendations