Edge Detection based on Ant Colony Optimization Using Adaptive Thresholding Technique

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Pragya Gautam 1,* Krishna Raj 1

1. Dept. of Electronics Engineering, Harcourt Butler Technical University, Kanpur, 208002, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2018.07.07

Received: 24 Apr. 2018 / Revised: 3 May 2018 / Accepted: 16 May 2018 / Published: 8 Jul. 2018

Index Terms

Ant Colony Optimization (ACO), Edge Detection, Peak Signal to Noise Ratio (PSNR), Performance Ratio (PR), Efficiency (EF)


Image edge detection is a process where true edges of an image are identified. In past, gradient based methods in which first or second order pixel difference is used to find discontinuities and if magnitude value of gradient is higher than certain threshold then that pixel under observation is identified as edge pixel. These methods are full of error, because in addition to true edges they also find false edges and infect false edges are more in comparison to true edges. To solve such problem, swarm intelligence based ant colony optimization based edge detection method is detailed where numbers of falsely detected edges are very small. The performance of the ant colony optimization (ACO) is done in terms of Peak Signal to Noise Ratio, Performance Ratio and Efficiency.

Cite This Paper

Pragya Gautam, Krishna Raj, " Edge Detection based on Ant Colony Optimization Using Adaptive Thresholding Technique ", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.7, pp. 60-68, 2018. DOI: 10.5815/ijigsp.2018.07.07


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