---
res:
  bibo_abstract:
  - Nonlinear spatial transforms and fuzzy pattern classification with unimodal potential
    functions are already established in signal processing. They have proved to be
    excellent tools in feature extraction and classification. We propose an inspection
    method for pattern recognition and classification of two dimensional translation
    variant security elements such as stripes, kinegrams and others, which are widely
    used as applications in bank note printing. The system is based on discrete non
    linear translation invariant circular transforms and fuzzy pattern classification.
    Nonlinear discrete circular transforms are adaptable transforms, which can be
    optimized for different application tasks, such as translation variant object
    analysis and position location. They are mainly used as generators for feature
    vectors. Even though, the feature vector is theoretically translation invariant,
    the object movement creates a translation tolerant feature vector, because in
    real systems and applications many problems can occur, such as signal and optical
    distortions. Therefore, the features should be further analysed by a fuzzy pattern
    classifier. Implementation of the transforms and fuzzy pattern classifier in radix-2-structures
    is possible, allowing fast calculations with a computational complexity of O(N)
    up to O(Nld(N)). Furthermore, the algorithms can be implemented in one Field Programmable
    Gate Array (FPGA), which operates with 40 MHz clock rate.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Thomas
      foaf_name: Türke, Thomas
      foaf_surname: Türke
  - foaf_Person:
      foaf_givenName: Volker
      foaf_name: Lohweg, Volker
      foaf_surname: Lohweg
      foaf_workInfoHomepage: http://www.librecat.org/personId=1804
    orcid: 0000-0002-3325-7887
  bibo_doi: dx.doi.org/10.1117/12.527452
  dct_date: 2004^xs_gYear
  dct_language: eng
  dct_publisher: SPIE@
  dct_title: Real-time image-processing-system-on-chip for security feature detection
    and classification@
...