• Title/Summary/Keyword: Kullbak-Leibler Distance

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GMM-KL Framework for Indoor Scene Matching (실내 환경 이미지 매칭을 위한 GMM-KL프레임워크)

  • Kim, Jun-Young;Ko, Han-Seok
    • Proceedings of the KIEE Conference
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    • 2005.10b
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    • pp.61-63
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    • 2005
  • Retreiving indoor scene reference image from database using visual information is important issue in Robot Navigation. Scene matching problem in navigation robot is not easy because input image that is taken in navigation process is affinly distorted. We represent probabilistic framework for the feature matching between features in input image and features in database reference images to guarantee robust scene matching efficiency. By reconstructing probabilistic scene matching framework we get a higher precision than the existing feaure-feature matching scheme. To construct probabilistic framework we represent each image as Gaussian Mixture Model using Expectation Maximization algorithm using SIFT(Scale Invariant Feature Transform).

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