• 제목/요약/키워드: Kullbak-Leibler Distance

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

  • Kim, Jun-Young;Ko, Han-Seok
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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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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