Statistical Pattern Recognition Using the Normalized Complex Moment Components Vector

  • Stavros Paschalakis
  • Peter Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

This paper presents a new feature vector for statistical pattern recognition based on the theory of moments, namely the Normalized Complex Moment Components (NCMC). The NCMC will be evaluated in the recognition of objects which share identical silhouettes using grayscale images and its performance will be compared with that of a commonly used moment based feature vector, the Hu moment invariants. The tolerance of the NCMC to random noise and the effect of using different orders of moments in its calculation will also be investigated.

Keywords

Feature Vector Reference Vector Statistical Pattern Recognition Geometric Moment Noise Tolerance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Stavros Paschalakis
    • 1
  • Peter Lee
    • 1
  1. 1.Electronic Engineering LaboratoryUniversity of Kent at CanterburyCanterburyUK

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