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)

Abstract

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.

Keywords

Unsupervised Anomaly Detection Novelty Detection Texture Segmentation Volumetric Segmentation Data Mining 

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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

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