View in EDS PDF Full Text

An Experimental Study for the Effects of Noise on Pattern Recognition Algorithms.

Saved in:
Bibliographic Details
Title: An Experimental Study for the Effects of Noise on Pattern Recognition Algorithms.
Authors: Guang Yi Chen1 guang_c@cse.concordia.ca, Krzyzak, Adam2 krzyzak@cse.concordia.ca
Source: Engineering Letters. May2025, Vol. 33 Issue 5, p1185-1192. 8p.
Subjects: Pattern recognition systems, Additive white Gaussian noise, Fast Fourier transforms, Discrete wavelet transforms, Central processing units
Abstract: Pattern recognition is a very important topic in computer vision. Among existing methods, which one is the most robust to noise? This is a very interesting question to answer. In this paper, we compare fifteen different methods for pattern recognition under different noise levels and different rotation angles. Most of these methods are invariant to translation, rotation, and scaling of the pattern images. Our experiments demonstrate that the Ridgelet + FFT (fast Fourier transform) descriptor is the most robust to additive Gaussian white noise (AGWN) for both a printed Chinese character dataset and an aircraft dataset. In addition, the Zernike moments and the Radon transform + FFT2 descriptor are also relatively robust to noise, but they are not as good as the Ridgelet + FFT descriptor for pattern recognition. We also compare the CPU (central processing units) computational time for all these methods for both pattern datasets. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) 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.)
Database: Engineering Source
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 185023769
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: An Experimental Study for the Effects of Noise on Pattern Recognition Algorithms.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Guang+Yi+Chen%22">Guang Yi Chen</searchLink><relatesTo>1</relatesTo><i> guang_c@cse.concordia.ca</i><br /><searchLink fieldCode="AR" term="%22Krzyzak%2C+Adam%22">Krzyzak, Adam</searchLink><relatesTo>2</relatesTo><i> krzyzak@cse.concordia.ca</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. May2025, Vol. 33 Issue 5, p1185-1192. 8p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Additive+white+Gaussian+noise%22">Additive white Gaussian noise</searchLink><br /><searchLink fieldCode="DE" term="%22Fast+Fourier+transforms%22">Fast Fourier transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete+wavelet+transforms%22">Discrete wavelet transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Central+processing+units%22">Central processing units</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Pattern recognition is a very important topic in computer vision. Among existing methods, which one is the most robust to noise? This is a very interesting question to answer. In this paper, we compare fifteen different methods for pattern recognition under different noise levels and different rotation angles. Most of these methods are invariant to translation, rotation, and scaling of the pattern images. Our experiments demonstrate that the Ridgelet + FFT (fast Fourier transform) descriptor is the most robust to additive Gaussian white noise (AGWN) for both a printed Chinese character dataset and an aircraft dataset. In addition, the Zernike moments and the Radon transform + FFT2 descriptor are also relatively robust to noise, but they are not as good as the Ridgelet + FFT descriptor for pattern recognition. We also compare the CPU (central processing units) computational time for all these methods for both pattern datasets. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=185023769
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 1185
    Subjects:
      – SubjectFull: Pattern recognition systems
        Type: general
      – SubjectFull: Additive white Gaussian noise
        Type: general
      – SubjectFull: Fast Fourier transforms
        Type: general
      – SubjectFull: Discrete wavelet transforms
        Type: general
      – SubjectFull: Central processing units
        Type: general
    Titles:
      – TitleFull: An Experimental Study for the Effects of Noise on Pattern Recognition Algorithms.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Guang Yi Chen
      – PersonEntity:
          Name:
            NameFull: Krzyzak, Adam
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1816093X
          Numbering:
            – Type: volume
              Value: 33
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Engineering Letters
              Type: main
ResultId 1