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title

Noisy images edge detection: Ant colony optimization algorithm

Credit to Download: 1 | Page Numbers 7 | Abstract Views: 40
Year: 2016
COI code: JR_JADM-4-1_009
Paper Language: English

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Authors Noisy images edge detection: Ant colony optimization algorithm

  Z. Dorrani - Department of Electrical Engineering, Payame Noor University (PNU), Tehran, Iran.
  M.S. Mahmoodi - Department of Computer Engineering, Payame Noor University (PNU), Tehran, Iran.

Abstract:

The edges of an image define the image boundary. When the image is noisy, it does not become easy to identify the edges. Therefore, a method requests to be developed that can identify edges clearly in a noisy image. Many methods have been proposed earlier using filters, transforms and wavelets with Ant colony optimization (ACO) that detect edges. We here used ACO for edge detection of noisy images with Gaussian noise and salt and pepper noise. As the image edge frequencies are close to the noise frequency band, the edge detection using the conventional edge detection methods is challenging. The movement of ants depends on local discrepancy of image’s intensity value. The simulation results compared with existing conventional methods and are provided to support the superior performance of ACO algorithm in noisy images edge detection. Canny, Sobel and Prewitt operator have thick, non continuous edges and with less clear image content. But the applied method gives thin and clear edges.

Keywords:

Ant Colony, Edge Detection, Gaussian Noise, Noisy Image, Salt and Pepper Noise

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COI code: JR_JADM-4-1_009

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Dorrani, Z. & M.S. Mahmoodi, 2016, Noisy images edge detection: Ant colony optimization algorithm, Journal of Artificial Intelligence & Data Mining 4 (1), https://www.civilica.com/Paper-JR_JADM-JR_JADM-4-1_009.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Dorrani, Z. & M.S. Mahmoodi, 2016)
Second and more: (Dorrani & Mahmoodi, 2016)
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The University/Research Center Information:
Type: Payame Noor University
Paper No.: 47013
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