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Comparative Analysis of common Edge Detection Algorithms using Pre-processing Technique

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 7 No. 5 (2017)

DOI
10.11591/ijece.v7i5.pp2574-2580
ISSN
2088-8708, 2088-8708
Published
2017-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Edge detection is the process of segmenting an image by detecting discontinuities in brightness. So far, several standard segmentation methods have been widely used for edge detection. However, due to inherent quality of images, these methods prove ineffective if they are applied without any preprocessing. In this paper, an image preprocessing approach has been adopted in order to get certain parameters that are useful to perform better edge detection with the standard edge detection methods. The proposed preprocessing approach involves median filtering to reduce the noise in image and then Edge Detection technique is carried out. And atlast Standard edge detection methods can be applied to the resultant preprocessing image and its Simulation results are show that our preprocessed approach when used with a standard edge detection method enhances its performance.

Collaboration network of the authors

6 authors · 15 co-authorship links

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  • Prasad V. Potluri Siddhartha Institute of Technology
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Comparative Analysis of common Edge Detection Algorithms using Pre-processing Technique</dc:title><dc:creator>Kumar Reddy, R. Vijaya</dc:creator><dc:creator>Raju, K. Prudvi</dc:creator><dc:creator>Kumar, M. Jogendra</dc:creator><dc:creator>Kumar, L. Ravi</dc:creator><dc:creator>Prakash, P Ravi</dc:creator><dc:creator>Kumar, S. Sai</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;p&amp;gt;Edge detection is the process of segmenting an image by detecting discontinuities in brightness. So far, several standard segmentation methods have been widely used for edge detection. However, due to inherent quality of images, these methods prove ineffective if they are applied without any preprocessing. In this paper, an image preprocessing approach has been adopted in order to get certain parameters that are useful to perform better edge detection with the standard edge detection methods. The proposed preprocessing approach involves median filtering to reduce the noise in image and then Edge Detection technique is carried out. And atlast Standard edge detection methods can be applied to the resultant preprocessing image and its Simulation results are show that our preprocessed approach when used with a standard edge detection method enhances its performance.&amp;lt;/p&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2017-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v7i5.pp2574-2580</dc:identifier><dc:identifier>10.11591/ijece.v7i5.pp2574-2580</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 7 No 5 (2017); 2574</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/8210/6973</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>