Color Image Edge Detection Method Based on the Improved Whale Optimization Algorithm

Liu, Dujin; Zhou, Shi‐Ping; Shen, Rong; Luo, Xichun

doi:10.1109/access.2023.3236761

Cited by 11 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Results demonstrate that the SiWOA could best solve the DPS problem. Liu et al [ 182 ] proposed an improved whale optimization algorithm to obtain the preliminary edge of unmanned aerial vehicle-captured color images. This method applies the global random position and information exchange mechanisms into the random walk foraging formula of the canonical WOA.…”

Section: Improved Woa Variantsmentioning

confidence: 99%

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Nadimi-Shahraki

Zamani

Varzaneh

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.

show abstract

Section: Improved Woa Variantsmentioning

confidence: 99%

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Nadimi-Shahraki

Zamani

Varzaneh

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…In comparison to other methods [23,25,36,40,46], the distinctiveness of this approach lies in its utilization of fractional derivatives and the development of a thresholding technique based on the mean fractional-order gradient, which collectively contribute to improved edge extraction and representation. The experimental validation further strengthens the credibility of the proposed method, suggesting its viability for practical applications in image processing and computer vision.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fractional Edge Detector Based on Generalized Fractional Operator

Elgezouli,

Abdoon,

Belhaouari

et al. 2024

Eur. J. Pure Appl. Math.

View full text Add to dashboard Cite

This work pioneers a novel approach in image edge detection through the utilization of the generalized fractional operator. By harnessing the global attributes inherent in fractional derivatives, it aims to enhance the extraction of intricate edge details.This is accomplished by creating the mask by using fractional derivative and adapt the mask by another parameter, yielding compelling and informative edge representations, as validated by experimental results. This advancement not only augments computer vision and image analysis techniques but also holds promise for refining image processing methodologies. Future endeavors may explore its adaptability across diverse imaging domains like medical and satellite imagery, while integration into deep learning frameworks could elevate its potential for advanced feature extraction and deeper image understanding. Additionally, optimizing its computational efficiency would broaden its scope for real-time deployment in fields such as robotics and autonomous systems.

show abstract

“…They are employed in border control and immigration processes to verify the identities of travelers, ensuring that no individual can fraudulently use another person's documents to bypass security checks. Facial recognition technologies also play a pivotal role in the digital financial landscape [17]. They are used to authenticate users during online banking, payment authorizations, and mobile payment applications, thereby preventing fraudulent activities.…”

Section: Introductionmentioning

confidence: 99%

Spoofify: A Hybrid Anti-Spoofing Facial Recognition System

Zia,

Ejaz,

Ahmad

et al. 2024

Preprint

View full text Add to dashboard Cite

Facial recognition systems have gained widespread adoption as replacements for conventional authenti- cation methods due to their robustness, offering bet- ter security compared to systems such as PINs, pass- words, and fingerprints. However, despite their re- silience, facial recognition remains vulnerable to var- ious attacks , including picture, video, and replay at- tacks. To mitigate these risks, the implementation of a robust anti-spoofing mechanism is imperative. In this paper, we propose a novel hybrid anti-spoofing facial recognition system that combines unsupervised and supervised approaches to address spoofing vul- nerabilities effectively. Leveraging Generative Adver- sarial Networks (GANs) as the cornerstone model for anti-spoofing, our system employs their efficiency in feature learning to discriminate between genuine and spoofed images. The user identification is performed using a K-Nearest Neighbors (KNN) classifier, which analyzes facial features to match individuals against a database of registered users. We evaluate the perfor- mance of our proposed system on the CelebA-Spoof and CASIA-FASD Datasets, achieving a commend- able accuracy rate of 69% on CASIA-FASD. Through rigorous experimentation and analysis, we demon- strate the efficacy and reliability of our hybrid anti- spoofing facial recognition system, offering a promis- ing solution to enhance security in real-world appli- cations. The source code for the approach will be re- leased on: https://github.com/ayeshazia99/spoofify- a-hybrid-antispoofing-facial-recognition-system

show abstract

Color Image Edge Detection Method Based on the Improved Whale Optimization Algorithm

Cited by 11 publications

References 13 publications

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

A Novel Fractional Edge Detector Based on Generalized Fractional Operator

Spoofify: A Hybrid Anti-Spoofing Facial Recognition System

Contact Info

Product

Resources

About