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Interest Points via Maximal Self-Dissimilarities

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9004))

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Abstract

We propose a novel interest point detector stemming from the intuition that image patches which are highly dissimilar over a relatively large extent of their surroundings hold the property of being repeatable and distinctive. This concept of contextual self-dissimilarity reverses the key paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local Means filter, which build upon the presence of similar - rather than dissimilar - patches. Moreover, our approach extends to contextual information the local self-dissimilarity notion embedded in established detectors of corner-like interest points, thereby achieving enhanced repeatability, distinctiveness and localization accuracy.

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Notes

  1. 1.

    Interest point detection is run at multiple scales as described in Sect. 3.

  2. 2.

    http://www.vision.ee.ethz.ch/surf/.

  3. 3.

    http://vision.deis.unibo.it/ssalti/Wave.

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Author information

Authors and Affiliations

  1. DISI, University of Bologna, Bologna, Italy

    Federico Tombari & Luigi Di Stefano

Authors
  1. Federico Tombari
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  2. Luigi Di Stefano
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Corresponding author

Correspondence to Federico Tombari .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Tombari, F., Di Stefano, L. (2015). Interest Points via Maximal Self-Dissimilarities. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_39

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  • DOI: https://doi.org/10.1007/978-3-319-16808-1_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16807-4

  • Online ISBN: 978-3-319-16808-1

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