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Intensity Order-Based Local Descriptors

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Local Image Descriptor: Modern Approaches

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

In this chapter, we discuss the local descriptors which exploit intensity order information. The good property of intensity order is that it is invariant to monotonic brightness change, which has been well studied in recent years, leading to the state-of-the-art descriptors for image matching. It has been used in both feature construction and feature pooling. We first discuss the most straightforward method using intensity order information in a local image patch, which creates a 2D histogram of intensity order and location for feature description. Next, we elaborate a new feature description framework by intensity order-based pooling. Then, a local intensity order pattern (LIOP) and how it is used in this framework are introduced. Finally, we demonstrate how we can design a binary descriptor by using the ordinal information in the local image patch.

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Notes

  1. 1.

    http://lear.inrialpes.fr/people/mikolajczyk/.

  2. 2.

    We will give a detailed introduction to them in Chap. 4.

  3. 3.

    The term ‘invariants’ mean that they are invariant to some certain kinds of geometric/photometric transformations.

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Bin Fan & Fuchao Wu

  2. School of EEE, Nanyang Technological University, Singapore, Singapore

    Zhenhua Wang

Authors
  1. Bin Fan
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  2. Zhenhua Wang
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  3. Fuchao Wu
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Correspondence to Bin Fan .

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Fan, B., Wang, Z., Wu, F. (2015). Intensity Order-Based Local Descriptors. In: Local Image Descriptor: Modern Approaches. SpringerBriefs in Computer Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49173-7_3

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  • DOI: https://doi.org/10.1007/978-3-662-49173-7_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49171-3

  • Online ISBN: 978-3-662-49173-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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