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Classification of Volumetric Data Using Multiway Data Analysis

  • Hayato ItohEmail author
  • Atsushi Imiya
  • Tomoya Sakai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

We introduce a method to extract compressed outline shapes of objects from global textures of volumetric data and to classify them by multiway tensor analysis. For the extraction of outline shapes, we applied three-way tensor principal component analysis to voxel images. A small number of major principal components represent the shape of objects in a voxel image. For the classification of objects, we use tensor subspace method. Using extracted outline shapes and tensor-based classification method, we achieve pattern recognition for volumetric data.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Graduate School of Advanced Integration ScienceChiba UniversityChibaJapan
  2. 2.Institute of Management and Information TechnologiesChiba UniversityChibaJapan
  3. 3.Graduate School of EngineeringNagasaki UniversityNagasakiJapan

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