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      Improved SIFT-Features Matching for Object Recognition

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      Visions of Computer Science - BCS International Academic Conference (VOCS)

      BCS International Academic Conference

      22 - 24 September 2008

      SIFT algorithm, Improved SIFT, Images matching, Object recognition

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          Abstract

          The SIFT algorithm (Scale Invariant Feature Transform) proposed by Lowe [1] is an approach for extracting distinctive invariant features from images. It has been successfully applied to a variety of computer vision problems based on feature matching including object recognition, pose estimation, image retrieval and many others. However, in real-world applications there is still a need for improvement of the algorithm’s robustness with respect to the correct matching of SIFT features. In this paper, an improvement of the original SIFT algorithm providing more reliable feature matching for the purpose of object recognition is proposed. The main idea is to divide the features extracted from both the test and the model object image into several sub-collections before they are matched. The features are divided into several sub-collections considering the features arising from different octaves, that is from different frequency domains.

          The SIFT algorithm (Scale Invariant Feature Transform) proposed by Lowe [1] is an approach for extracting distinctive invariant features from images. It has been successfully applied to a variety of computer vision problems based on feature matching including object recognition, pose estimation, image retrieval and many others. However, in real-world applications there is still a need for improvement of the algorithm’s robustness with respect to the correct matching of SIFT features. In this paper, an improvement of the original SIFT algorithm providing more reliable feature matching for the purpose of object recognition is proposed. The main idea is to divide the features extracted from both the test and the model object image into several sub-collections before they are matched. The features are divided into several sub-collections considering the features arising from different octaves, that is from different frequency domains.

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          Author and article information

          Contributors
          Conference
          September 2008
          September 2008
          : 179-190
          Affiliations
          Institute of Automation, University of Bremen, FB1 / NW1

          Otto-Hahn-Allee 1

          D-28359 Bremen
          Article
          10.14236/ewic/VOCS2008.16
          © Faraj Alhwarin et al. Published by BCS Learning and Development Ltd. Visions of Computer Science - BCS International Academic Conference

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Visions of Computer Science - BCS International Academic Conference
          VOCS
          Imperial College, London, UK
          22 - 24 September 2008
          Electronic Workshops in Computing (eWiC)
          BCS International Academic Conference
          Product
          Product Information: 1477-9358BCS Learning & Development
          Self URI (journal page): https://ewic.bcs.org/
          Categories
          Electronic Workshops in Computing

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