Multi-circle Detection Using Multimodal Optimization

Çetin, A. Enis; Rezai, Somaiyeh; Akan, Taymaz

doi:10.1007/978-3-031-16832-1_11

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…All the evolutionary algorithms used in this experimental study, have been proposed to solve multi-circle detection problem. DE [31], HS [33], and GWO [35] by a unimodal fashion, and MPSO considering a multimodal perspective [37].…”

Section: Resultsmentioning

confidence: 99%

Multi-Circle Detection Guided by Multimodal Optimization Scheme

et al. 2023

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Automatic circle detection is one of the most important construction elements to design more complex industrial image tasks such as target detection, manufacture inspection and process control. Most of the literature concerning multi-circle detection can be categorized under deterministic or stochastic perspectives. Deterministic approaches combine geometric and histogram information to identify circle shapes. However, deterministic techniques are unable to detect circles in presence of noise, shape variance, or occlusion. On the other hand, stochastic methodologies such as metaheuristic algorithms have been also proposed as alternatives to deterministic approaches for circle detection. Although these techniques are in general more robust, they allow detecting only one circle per execution, translating the detection problem into a unimodal optimization problem. Under such conditions, it is necessary to execute multiple times the algorithm to identify all the circles contained in the image. In this paper, the multi-circle detection problem has been reformulated as a multimodal optimization problem. The proposed approach adopts the Multimodal Flower Pollination Algorithm (MFPA) to detect all the circular instances contained in the image. Experimental results suggest that the proposed approach significantly improves the multi-circle detection problem.

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