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Low-pass Filter’s Effects on Image Analysis Using Subspace Classifier

  • Nobuo Matsuda
  • Fumiaki Tajima
  • Naoki Miyatake
  • Hideaki Sato
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 268)

Abstract

This paper shows an effect for applying a low-pass filter on the performance of image analysis using the Subspace classifier. The feature extraction was firstly based on three kinds of intensity distributions, and the feature vector and subspace dimension for recognition were examined. Afterwards, a series of the analysis on the accuracies were conducted in the cases of filtered images and without filtered. The analyzed accuracies by using the Subspace classifier were also compared with the results by the technique of another: Learning vector quantization (LVQ).

Keywords

Subspace Classifier Feature Space Low-pass Filter Learning Vector Quantization Fundus Image 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nobuo Matsuda
    • 1
  • Fumiaki Tajima
    • 2
  • Naoki Miyatake
    • 3
  • Hideaki Sato
    • 4
  1. 1.Dept. of Electronic and Mechanical EngineeringOshima National College of Maritime TechnologyOshima-gunJapan
  2. 2.Education and Human ScienceYokohama National UniversityHodogayaJapan
  3. 3.Chiba Institute of ScienceChibaJapan
  4. 4.Federation of National Public Service Personnel Mutual Aid AssociationTokyoJapan

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