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Automatic estimation of a scale resolution in forensic images

Gertsovich, I.; Nilsson, M. LU ; Bartůněk, J. S. and Claesson, I. LU (2018) In Forensic Science International 283. p.58-71
Abstract

This paper proposes a new method for an automatic detection of a resolution of a scale or a ruler with graduation marks in the shoeprint images. The method creates a vector of the correlations estimated from the co-occurrence matrices for every row in a shoeprint image. The scale resolution is estimated from maxima in Fourier spectrum of the correlations’ vectors. The proposed method is evaluated on over 500 images taken at crime scenes and in a forensics laboratory. The experimental results indicate the possibility of applying the proposed method to automatically estimate the scale resolution in forensic images. The automatic detection of a scale resolution could be used to automatically rescale a forensic image before the printing... (More)

This paper proposes a new method for an automatic detection of a resolution of a scale or a ruler with graduation marks in the shoeprint images. The method creates a vector of the correlations estimated from the co-occurrence matrices for every row in a shoeprint image. The scale resolution is estimated from maxima in Fourier spectrum of the correlations’ vectors. The proposed method is evaluated on over 500 images taken at crime scenes and in a forensics laboratory. The experimental results indicate the possibility of applying the proposed method to automatically estimate the scale resolution in forensic images. The automatic detection of a scale resolution could be used to automatically rescale a forensic image before the printing this image in “one-to-one” scale. Furthermore, the proposed method could be used to automatically rescale images to an equal scale thus allowing to compare the images digitally.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Gray level co-occurrence matrix, Near regular texture, Scale resolution estimation, Shoeprint image, Texture pattern periodicity
in
Forensic Science International
volume
283
pages
14 pages
publisher
Elsevier
external identifiers
  • scopus:85038855166
ISSN
0379-0738
DOI
10.1016/j.forsciint.2017.12.007
language
English
LU publication?
yes
id
b427cc5f-5826-422c-ae1c-d3dccc43814b
date added to LUP
2018-01-31 09:23:35
date last changed
2018-05-29 12:27:55
@article{b427cc5f-5826-422c-ae1c-d3dccc43814b,
  abstract     = {<p>This paper proposes a new method for an automatic detection of a resolution of a scale or a ruler with graduation marks in the shoeprint images. The method creates a vector of the correlations estimated from the co-occurrence matrices for every row in a shoeprint image. The scale resolution is estimated from maxima in Fourier spectrum of the correlations’ vectors. The proposed method is evaluated on over 500 images taken at crime scenes and in a forensics laboratory. The experimental results indicate the possibility of applying the proposed method to automatically estimate the scale resolution in forensic images. The automatic detection of a scale resolution could be used to automatically rescale a forensic image before the printing this image in “one-to-one” scale. Furthermore, the proposed method could be used to automatically rescale images to an equal scale thus allowing to compare the images digitally.</p>},
  author       = {Gertsovich, I. and Nilsson, M. and Bartůněk, J. S. and Claesson, I.},
  issn         = {0379-0738},
  keyword      = {Gray level co-occurrence matrix,Near regular texture,Scale resolution estimation,Shoeprint image,Texture pattern periodicity},
  language     = {eng},
  month        = {02},
  pages        = {58--71},
  publisher    = {Elsevier},
  series       = {Forensic Science International},
  title        = {Automatic estimation of a scale resolution in forensic images},
  url          = {http://dx.doi.org/10.1016/j.forsciint.2017.12.007},
  volume       = {283},
  year         = {2018},
}