Detection of Important Features from Images Using Heuristic Approach

Połap, Dawid; Woźniak, Marcin

doi:10.1007/978-3-319-67642-5_36

Cited by 3 publications

(3 citation statements)

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“…Połap and Woźniak [42] used dragonfly to explore the pixels in images and assess which of these pixels represent significant components of the objects. Therefore, this technique works as a detection model for finding interesting features.…”

Section: Applications Of Damentioning

confidence: 99%

“…The correlation coefficient values using the DA were greater than the other techniques such as PSO, GA, and random selection. [43] Exploring the pixels of images and discovering which pixel contains significant information about the object. (DA was used as a detection model)…”

Section: Referencementioning

confidence: 99%

“…In terms of performance metrics, a comparative analysis was carried out in this work. The metrics used were correlation coefficient and peak signal-to-noise ratio (PSNR Reference [43] used dragonfly to explore the pixels in images and assess which of these pixels represent significant components of the objects. Therefore, this technique works as a detection model for finding interesting features.…”

Section: A Image Processingmentioning

confidence: 99%

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Dragonfly Algorithm and Its Applications in Applied Science Survey

Rahman

Rashid

2019

Computational Intelligence and Neuroscience

104

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One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm's weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.

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Section: Applications Of Damentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Section: A Image Processingmentioning

confidence: 99%

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