NOISY IMAGES EDGE DETECTION: ANT COLONY OPTIMIZATION ALGORITHM

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DORRANI Z.; MAHMOODI M.S.

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Journal Paper

Paper Information

Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2016 | Volume:4 | Issue:1 Page(s): 77-83

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Information Journal Paper

Title

NOISY IMAGES EDGE DETECTION: ANT COLONY OPTIMIZATION ALGORITHM

Author(s)

DORRANI Z. | MAHMOODI M.S. | Issue Writer Certificate

Keywords

ANT COLONYQ2

EDGE DETECTIONQ2

GAUSSIAN NOISEQ2

NOISY IMAGEQ2

SALT AND PEPPER NOISEQ2

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.

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APA: Copy

DORRANI, Z., & MAHMOODI, M.S.. (2016). NOISY IMAGES EDGE DETECTION: ANT COLONY OPTIMIZATION ALGORITHM. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, 4(1), 77-83. SID. https://sid.ir/paper/255389/en

Vancouver: Copy

DORRANI Z., MAHMOODI M.S.. NOISY IMAGES EDGE DETECTION: ANT COLONY OPTIMIZATION ALGORITHM. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING[Internet]. 2016;4(1):77-83. Available from: https://sid.ir/paper/255389/en

IEEE: Copy

Z. DORRANI, and M.S. MAHMOODI, “NOISY IMAGES EDGE DETECTION: ANT COLONY OPTIMIZATION ALGORITHM,” JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, vol. 4, no. 1, pp. 77–83, 2016, [Online]. Available: https://sid.ir/paper/255389/en

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