Robust Circle Detection Using Harmony Search

Fourie, Jaco

doi:10.1155/2017/9710719

Cited by 11 publications

(9 citation statements)

References 29 publications

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“…In comparison, all the five metaheuristic methods have been executed considering a fixed number of 1000 iterations (k = Maxgen). This stop criterion has been established to maintain compatibility with other similar works found in the literature [7][8][9][10][11][12][13]. To minimize the random influence in the results, each experiment is tested for 30 independent executions.…”

Section: Detection In Natural Imagesmentioning

confidence: 99%

“…Therefore, conducted by the values of the matching function, the set of encoded points are operated through a particular metaheuristic approach so that the best solutions represent the original shapes inside the image. Different pioneer metaheuristic algorithms have been used to produce several interesting shape detectors such as Genetic algorithms (GAs) [5,6] and Particle Swarm Optimization (PSO) [7], Differential Evolution (DE) [8], Cloning Selection method (CSM) [9], Harmony Search (HS) [10], Artificial Bee Colony (ABC) [11] and Animal Behavior (AB) [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images

Cuevas¹,

Trujillo²,

Navarro³

et al. 2020

IJCIS

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Shape recognition in images represents one of the complex and hard-solving problems in computer vision due to its nonlinear, stochastic and incomplete nature. Classical image processing techniques have been normally used to solve this problem. Alternatively, shape recognition has also been conducted through metaheuristic algorithms. They have demonstrated to have a competitive performance in terms of robustness and accuracy. However, all of these schemes use old metaheuristic algorithms as the basis to identify geometrical structures in images. Original metaheuristic approaches experiment several limitations such as premature convergence and low diversity. Through the introduction of new models and evolutionary operators, recent metaheuristic methods have addressed these difficulties providing in general better results. This paper presents a comparative analysis on the application of five recent metaheuristic schemes to the shape recognition problem such as the Grey Wolf Optimizer (GWO), Whale Optimizer Algorithm (WOA), Crow Search Algorithm (CSA), Gravitational Search Algorithm (GSA) and Cuckoo Search (CS). Since such approaches have been successful in several new applications, the objective is to determine their efficiency when they face a complex problem such as shape detection. Numerical simulations, performed on a set of experiments composed of images with different difficulty levels, demonstrates the capacities of each approach.

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Section: Detection In Natural Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images

Cuevas¹,

Trujillo²,

Navarro³

et al. 2020

IJCIS

View full text Add to dashboard Cite

show abstract

“…The work in [27], on the other hand, store edgels locations randomly within the set. These particular organisations, as remarked in [26], do not ensure that nearby pixels within the edge image are neighbours in the search space. This is an important issue for our method since the TLBO algorithm locates better individuals in each iteration by shifting them towards the position of the best one.…”

Section: Preprocessingmentioning

confidence: 99%

“…Examples of meta‐heuristic algorithms include the genetic algorithm (GA), differential evolution (DE), the particle swarm optimisation (PSO), the artificial bee colony (ABC), the learning automata (LA) algorithm, the harmony search (HS), and the grenade explosion method. The utilisation of meta‐heuristic algorithms for circle detection has been reported, for example, using a GA [22], the PSO [23], an ABC [24], DE [25], the HS algorithm [26], and the LA algorithm [27]. For the aforementioned work, a set of parameters must be tuned appropriately to accomplish good accuracy and high detection rate in the circle detection task.…”

Section: Introductionmentioning

confidence: 99%

Automatic multi‐circle detection on images using the teaching learning based optimisation algorithm

López

Cuevas

2018

IET Computer Vision

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Circle detection has numerous applications towards industry, robotics, and science in general. Therefore, a significant effort has been made in order to develop an accurate and fast method for circle extraction. Commonly, different techniques such as the ones based on the Hough transform have been widely used because of their robustness. However, these techniques usually demand a considerable computational load and large storage, and therefore meta-heuristic approaches such as evolutionary and swarm-based algorithms have been studied as an alternative. This study introduces a circle-detection method based on a recently proposed meta-heuristic technique: the teaching learning based optimisation algorithm, which is a population-based technique that is inspired by the teaching and learning processes. The algorithm uses the encoding of three points as candidate circles over the edge image. To evaluate if such candidate circles are actually present within the edge map, an objective function is used to guide the search. To validate the efficacy of the proposed approach, several tests using noisy and complex images as input were carried out, and the results were compared with different approaches for circle detection.

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“…In the literature [18], a new dynamic clustering algorithm based on the hybridization of Harmony Search (HS) and fuzzy c-means to automatically segment MRI brain images in an intelligent manner was presented. Improvements to Harmony Search (HS) in circle detection were proposed that enable the algorithm to robustly find multiple circles in larger data sets and still work on realistic images that are heavily corrupted by noisy edges [19]. A new approach to estimate the vanishing point using a harmony search (HS) algorithm was proposed in [20].…”

Section: Introductionmentioning

confidence: 99%

Harmony Search Method with Global Sharing Factor Based on Natural Number Coding for Vehicle Routing Problem

et al. 2020

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This paper proposes an improved Harmony Search algorithm, and gives the definition of the Global Sharing Factor of the Harmony Search (HS) algorithm. In the definition, the number of creations of the HS algorithm is applied to the sharing factor and calculated. In this algorithm, the natural harmony encoding method is used to encode the initial harmony, and the total path length of all vehicles is taken as the optimization objective function. A new harmony generation strategy is proposed as follows: each tone component in an evolution is calculated separately using the new learning strategy and update strategy. In the calculation process, the tone component is judged by whether it needs to be adjusted according to the adjustment strategy. In this way, the problems of singularity and randomness of the new harmony generation strategy of basic HS are solved to improve the diversity of algorithm solutions. Then, a new Harmony Search method with Global Sharing Factor based on natural number coding and decoding for the Vehicle Routing Problem (GSF-HS-VRP) is proposed. The improved Global Sharing Factor-Harmony Search-Vehicle Routing Problem (GSF-HS-VRP) algorithm is applied to capacity-limited vehicle path optimization problems compared with the HS, Improved Harmony Search (IHS), Global-best Harmony Search (GHS), and Self-adaptive Global Best Harmony Search (SGHS) algorithms. The small-scale data and Solomon examples were adopted as the experimental data. Compared with the other four algorithms, the GSF-HS-VRP algorithm has the shortest running time, more rapid convergence speed, and higher efficiency. In the multi-vehicle test, with the increase of the number of vehicles, the optimized path of the vehicle is more satisfied in relation to the actual needs of customers. The results showed that this method could effectively improve the optimization performance of the capacity-limited vehicle routing problem.

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Robust Circle Detection Using Harmony Search

Cited by 11 publications

References 29 publications

Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images

Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images

Automatic multi‐circle detection on images using the teaching learning based optimisation algorithm

Harmony Search Method with Global Sharing Factor Based on Natural Number Coding for Vehicle Routing Problem

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