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Selective and Simple Graph Structures for Better Description of Local Point-Based Image Features

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 11114)

Abstract

The paper presents simple graph features based on a well-known image keypoints. We discuss the extraction method and geometrical properties that can be used. Chosen methods are tested in KNN tasks for almost 1000 object classes. The approach addresses problems in applications that cannot use learning methods explicitly, as real-time tracking, chosen object detection scenarios and structure from motion. Results imply that the idea is worth further research for chosen systems.

Keywords

  • Graphs Let
  • Deformable Part Model
  • Voting Space
  • Siamese Neural Network
  • Root Size

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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Correspondence to Grzegorz Kurzejamski .

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Kurzejamski, G., Iwanowski, M. (2018). Selective and Simple Graph Structures for Better Description of Local Point-Based Image Features. In: Chmielewski, L., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A., Petkov, N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science(), vol 11114. Springer, Cham. https://doi.org/10.1007/978-3-030-00692-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-00692-1_12

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