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Eurofuse 2011 pp 305-311 | Cite as

A PCA-Fuzzy Clustering Algorithm for Contours Analysis

  • Paulo Salgado
  • Getúlio Igrejas
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 107)

Abstract

Principal component analysis (PCA) is a usefully tool for data compression and information extraction. It is often utilized in point cloud processing as it provides an efficient method to approximate local point properties through the examination of the local neighborhoods. This process does sometimes suffer from the assumption that the neighborhood contains only a single surface, when it may contain curved surface or multiple discrete surface entities, as well as relating the properties from PCA to real world attributes. This paper will present a new method that joins the fuzzy clustering algorithm with a local sliding PCA analysis to identify the non-linear relations and to obtain morphological information of the data. The proposed PCA-Fuzzy algorithm is performed on the neighborhood of the cluster center and normal approximations in order to estimate a tangent surface and the radius of the curvature that characterizes the trend and curvature of the data points or contour regions.

Keywords

Principal Component Analysis Fuzzy Cluster Fuzzy Cluster Algorithm Fuzzy Support Vector Machine Robust Principal Component Analysis 
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 2011

Authors and Affiliations

  • Paulo Salgado
    • 1
  • Getúlio Igrejas
    • 2
  1. 1.Universidade de Trás-os-Montes e Alto DouroVila RealPortugal
  2. 2.Instituto Politécnico de BragançaBragançaPortugal

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