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Image Projective Invariants.

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Title: Image Projective Invariants.
Authors: Li, Erbo1 sophialiuli@gmail.com, Mo, Hanlin2 mohanlin@ict.ac.cn, Xu, Dong3 xudong0614@hotmail.com, Li, Hua2 lihua@ict.ac.cn
Source: IEEE Transactions on Pattern Analysis & Machine Intelligence. May2019, Vol. 41 Issue 5, p1144-1157. 14p.
Subjects: Differential invariants, Image processing, Chemical reactions, Nanoparticles, Numerical analysis
Abstract: In this paper, we have proved the existence of projective moment invariants of images using finite combinations of weighted moments, with relative projective differential invariants as weight functions. We have given some instances constructed in that way, and analyzed possible issues could affect the performance. Some procedures are taken to estimate partial derivatives of discrete images, and a new method is designed to normalize the number of pixels for discrete images to minimize the changes before and after the projective transformation. We have carried out experiments using popular image databases and real images to test the performance. And the results show that the invariants proposed in this paper have better stability and discriminability than other previously used moment invariants in image retrieval and classification. Users can directly extract invariant features of images for a given planar object from different viewpoints without knowing the parameters of the 2D projective transformations. Therefore, the projective moment invariant could be potentially useful for planar object recognition, image description and classification. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Pattern Analysis & Machine Intelligence is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Erbo%22">Li, Erbo</searchLink><relatesTo>1</relatesTo><i> sophialiuli@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mo%2C+Hanlin%22">Mo, Hanlin</searchLink><relatesTo>2</relatesTo><i> mohanlin@ict.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Dong%22">Xu, Dong</searchLink><relatesTo>3</relatesTo><i> xudong0614@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Hua%22">Li, Hua</searchLink><relatesTo>2</relatesTo><i> lihua@ict.ac.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Pattern+Analysis+%26+Machine+Intelligence%22">IEEE Transactions on Pattern Analysis & Machine Intelligence</searchLink>. May2019, Vol. 41 Issue 5, p1144-1157. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Differential+invariants%22">Differential invariants</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+reactions%22">Chemical reactions</searchLink><br /><searchLink fieldCode="DE" term="%22Nanoparticles%22">Nanoparticles</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink>
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  Label: Abstract
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  Data: In this paper, we have proved the existence of projective moment invariants of images using finite combinations of weighted moments, with relative projective differential invariants as weight functions. We have given some instances constructed in that way, and analyzed possible issues could affect the performance. Some procedures are taken to estimate partial derivatives of discrete images, and a new method is designed to normalize the number of pixels for discrete images to minimize the changes before and after the projective transformation. We have carried out experiments using popular image databases and real images to test the performance. And the results show that the invariants proposed in this paper have better stability and discriminability than other previously used moment invariants in image retrieval and classification. Users can directly extract invariant features of images for a given planar object from different viewpoints without knowing the parameters of the 2D projective transformations. Therefore, the projective moment invariant could be potentially useful for planar object recognition, image description and classification. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Pattern Analysis & Machine Intelligence is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1109/TPAMI.2018.2832060
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 1144
    Subjects:
      – SubjectFull: Differential invariants
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Chemical reactions
        Type: general
      – SubjectFull: Nanoparticles
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      – SubjectFull: Numerical analysis
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      – TitleFull: Image Projective Invariants.
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            NameFull: Mo, Hanlin
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            NameFull: Xu, Dong
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            NameFull: Li, Hua
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            – D: 01
              M: 05
              Text: May2019
              Type: published
              Y: 2019
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