Modified artificial bee colony algorithms for solving multiple circle detection problem

Aslan, Selçuk

doi:10.1007/s00371-020-01834-4

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…This algorithm can detect circles reliably even under obvious occlusion, discontinuity, or incomplete conditions, but it is not sensitive to small circles. Aslan 26 proposed two different recurrent candidate list generation methods that can identify circles at different locations, but they have some limitations regarding images containing overlapping pseudo-circles with similar radius values. González et al 27 proposed an evolutionary algorithm to automatically detect circles in images.…”

Section: Circle Detection Using the Intelligent Optimization Algorithmmentioning

confidence: 99%

“…When the large diameter circle has a large occlusion, the inscribed triangle method cannot ensure accuracy and effectiveness. There has also been extensive use of intelligent optimization algorithms in circle detection, such as genetic algorithms, 25 artificial bee colony algorithms, 26 evolutionary algorithms, 27 and deep learning 28 . Although these methods have shown good results in the application environment, there are still some limitations, including the inability to detect circles with small radii sufficiently and the lack of performance in the circle extraction of actual images.…”

Section: Introductionmentioning

confidence: 99%

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Circle detection algorithm based on neighborhood density clustering

Li,

Wang,

Zhang

et al. 2023

J. Electron. Imag.

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.Circle detection in images is one of the key technologies in machine vision, pattern recognition, and artificial intelligence. However, conventional circle detection methods are sensitive to complex scenes, noise, and occlusion in images. Solving the impact of these situations is still the focus of circle detection algorithm research. Therefore, a circle detection algorithm based on neighborhood density clustering (NDC) is proposed. The proposed algorithm calculates circle parameters of connected regions after extracting corners, corroding the corners, and marking the connected regions. Then, NDC of the circle parameters is executed to classify arcs belonging to the same circle into one category to acquire a virtual connected region. And the circle parameters of the virtual connected region are clustered using NDC again to obtain the circle parameter dataset of the circle to be detected. The precise circle parameters are further estimated by calculating the centroid of each category. To prevent false positives, candidate circles are verified through a ratio rule. Extensive experiments using both synthetic and real images were performed. The results compared with those of representative state-of-the-art methods demonstrate that the proposed algorithm can be applied to a variety of complex scenes and has several advantages: good anti-occlusion effect, more robustness against noise, high accuracy, and better performance.

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Section: Circle Detection Using the Intelligent Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Circle detection algorithm based on neighborhood density clustering

Li,

Wang,

Zhang

et al. 2023

J. Electron. Imag.

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“…EAI algorithms improve solutions to field-specific problems, such as the circle detection problem in images by using Bee Colony algorithms [29], detection of internal short circuit (ISC) within lithium-ion batteries [30], and detection of students who complete their undergraduate studies on time by predicting student's attrition rates [31]. In conclusion, it can be said that EAIs are not only optimization algorithms but are also increasingly applied to detection.…”

Section: Detectionmentioning

confidence: 99%

“…Zhang et al [60] applied the improved fitness function and improved update strategy of food source and improved the quality of the solution. Aslan [29] supplemented the ABC algorithm with two improvements; the first used only the most abundant food sources found by artificial bees, and the second used all food sources, even those left by bees. The combination of these improved solutions enables the use of an algorithm not only for optimization but also for detection.…”

Section: Colonymentioning

confidence: 99%

Application of Evolutionary Artificial Intelligence. An Exploratory Literature Review

Maknickienė¹

2022

ABIS

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Evolutionary processes found in nature are of interest to developers and practitioners of artificial intelligence because of the ability to optimize, detect, classify, and predict complex man-made processes. Evolutionary artificial intelligence (EAI) is examined from various perspectives to evaluate the main research directions and the trend of the decade. Co-occurrence networks were used to visualize data and find key sub-themes in a dataset consisting of article titles. The literature review covers the following aspects of EAI applications: methods, detection, data, approach, and colony. The resulting co-occurrence networks show a huge increase in diversity in research methods, data and function application possibilities, and approaches. Although simulating the behaviour of colonies is not as popular as it was a decade ago, the scope of applications for known algorithms has not been diminished.

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“…In addition to CHT, other circle detection methods have been developed based on different strategies such as random candidate location [10,11], the power of a point theorem [12], bee colony algorithms [13][14][15], and genetic algorithms (GAs) [16]. In particular, GAs appear among alternative strategies because they provide some of the most effective optimization and search algorithms within computational intelligence [17,18], as they have, among their main features, the ability to provide reliable solutions, adapt to varied problems, avoid local minima, and evaluate multiple solutions simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Hardware/Software Co-Design of a Circle Detection System Based on Evolutionary Computing

et al. 2022

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In recent years, the strategy of co-designing Hardware/Software (HW/SW) systems has been widely adopted to exploit the synergy between both approaches thanks to technological advances that have led to more powerful devices providing an increasingly better cost–benefit trade-off. This paper presents an HW/SW system for the detection of multiple circles in digital images based on a genetic algorithm. It is implemented on an Ultra96-v2 development board, which contains a Xilinx Zynq UltraScale+ MPSoC device and supports a Linux operating system that facilitates application development. The design is powered by developing an interactive computing environment by means of the Jupyter Notebook platform, in which different programming languages coexist. The specific advantages of each of these languages have been used to describe the hardware component that accelerates the evolutionary computation for circle detection (VHDL), to execute SW-HW interaction functions, as well as the pre- and post-processing of the images (ANSI-C) and to code, evaluate, and document the system execution process (Python). As a result, a computationally efficient application was obtained, with high accuracy in the detection of circles in synthetic and real images, and with a high degree of reconfigurability that provides the user with the necessary tools to incorporate it in a specific area of interest.

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Modified artificial bee colony algorithms for solving multiple circle detection problem

Cited by 7 publications

References 36 publications

Circle detection algorithm based on neighborhood density clustering

Circle detection algorithm based on neighborhood density clustering

Application of Evolutionary Artificial Intelligence. An Exploratory Literature Review

Hardware/Software Co-Design of a Circle Detection System Based on Evolutionary Computing

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