Edge detection in images using clustering algorithms

  • Authors:

  • Tuba Sirin;Mehmet Izzet Saglam;Isin Erer;Muhittin Gokmen

  • Affiliations:

  • Informatics Institute, Advanced Technologies in Engineering, Computer Science and Engineering, Istanbul Technical University, Maslak, Istanbul, Tureky;Informatics Institute, Advanced Technologies in Engineering, Satellite Communication and Remote Sensing Program, Istanbul Technical University, Maslak, Istanbul, Tureky;Faculty of Electrical and Electronic Engineering, Electronics and Communication Department, Istanbul Technical University, Maslak, Istanbul, Tureky;Faculty of Electrical and Electronic Engineering, Computer Engineering Department, Istanbul Technical University, Maslak, Istanbul, Tureky

  • Venue:
  • TELE-INFO'05 Proceedings of the 4th WSEAS International Conference on Telecommunications and Informatics
  • Year:
  • 2005

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Abstract

Edge detection is an important topic in image processing and a main tool in pattern recognition and image segmentation. Many edge detection techniques are available in the literature. 'A number of recent edge detectors are multiscale and include three main processing steps: smoothing, differentiation and labeling' (Ziau and Tabbone, 1997). This paper, presents a proposed method which is suitable for edge detection in images. This method is based on the use of the clustering algorithms (Self-Organizing Map (SOM), K-Means) and a gray scale edge detector (Canny, Generalized Edge Detector (GED)). It is shown that using the grayscale edge detectors may miss some parts of the edges which can be found using the proposed method.