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Artificial bee colony algorithm for enhancing image edge detection

  • Anan Banharnsakun
Original Paper
  • 27 Downloads

Abstract

The objective of computer image analysis and processing is to generate images with specific features that make them more suitable for humans and machines to observe and identify. Image edges are the most basic features of image analysis and processing; thus, in order to extract the edges from images, an operation known as “edge detection” is required. Applying an edge detector to an image will help to remove some information that may be regarded as less important, and therefore reduce the amount of data to be processed. As a result, the development of an effective method for edge detection is necessary. Recently, a number of techniques based on evolutionary computation have been applied in order to enhance various tasks in image processing. The artificial bee colony (ABC) algorithm is one of the more promising evolutionary computational approaches used to find an optimal solution. In this paper, an ABC algorithm for enhancing image edge detection is presented. In the proposed method, the ABC is employed to find an optimal edge filter and then, optimize the threshold value in the edge detection process. The experimental results demonstrate that the proposed approach works well for image edge detection with a reasonably high level of accuracy and outperforms existing algorithms.

Keywords

Artificial bee colony (ABC) Image edge enhancement Optimal image edge filter Optimal threshold selection Maximization of inter-class variance 

Notes

References

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computational Intelligence Research Laboratory (CIRLab), Computer Engineering Department, Faculty of Engineering at SrirachaKasetsart University Sriracha CampusChonburiThailand

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