Hybrid ACO algorithm for edge detection

Martinez, Cristian A.; Buemi, María Elena

doi:10.1007/s12530-019-09321-5

Cited by 2 publications

(1 citation statement)

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“…Subsequently, several scholars suggested initializing the heuristic function using statistical information extracted from an image. Martínez et al [27] initialized the heuristic function using a combination of mean and variance and compared its performance with Canny and gPb on images contaminated with Gaussian and salt-and-pepper noise. The results indicated that their approach exhibited robustness to noise.…”

Section: Introductionmentioning

confidence: 99%

Image Edge Detection Based on Fractional-Order Ant Colony Algorithm

Liu

2023

Fractal Fract

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Edge detection is a highly researched topic in the field of image processing, with numerous methods proposed by previous scholars. Among these, ant colony algorithms have emerged as a promising approach for detecting image edges. These algorithms have demonstrated high efficacy in accurately identifying edges within images. For this paper, due to the long-term memory, nonlocality, and weak singularity of fractional calculus, fractional-order ant colony algorithm combined with fractional differential mask and coefficient of variation (FACAFCV) for image edge detection is proposed. If we set the order of the fractional-order ant colony algorithm and fractional differential mask to ν = 0 , the edge detection method we propose becomes an integer-order edge detection method. We conduct experiments on images that are corrupted by multiplicative noise, as well as on an edge detection dataset. Our experimental results demonstrate that our method is able to detect image edges, while also mitigating the impact of multiplicative noise. These results indicate that our method has the potential to be a valuable tool for edge detection in practical applications.

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Section: Introductionmentioning

confidence: 99%