Edge Detection and Grey Level Co-Occurrence Matrix (GLCM) Algorithms for Fingerprint Identification

Winarno, Edy; Hadikurniawati, Wiwien; Wibisono, Setyawan; Septiarini, Anindita

doi:10.1109/icitech50181.2021.9590134

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…In (2), the absolute fractional forward difference can be derived when the coefficient in front of f (x − k∆x) is k+1) is a decreasing function about k. By using Equations ( 1) and (2), we can obtain the three fundamental properties, namely long-term memory, non-locality, and weak singularity, which are compared with Equation (3). These properties are attributed to the ability of Equations ( 1) and (2) to contain a significant amount of forward information and the fact that the fractional derivative of a constant (non-zero) is not zero.…”

Section: Fractional Calculusmentioning

confidence: 99%

“…Image edge detection is a critical technology in the field of digital image processing. This technique is applied in various domains such as fingerprint detection [1][2][3], face recognition [4][5][6], image segmentation [7][8][9][10], and more. Hence, the precision of edge detection is a crucial aspect of applications that rely on edge detection.…”

Section: Introductionmentioning

confidence: 99%

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Image Edge Detection Based on Fractional-Order Ant Colony Algorithm

Liu

2023

Fractal Fract

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Edge detection is a highly researched topic in the field of image processing, with numerous methods proposed by previous scholars. Among these, ant colony algorithms have emerged as a promising approach for detecting image edges. These algorithms have demonstrated high efficacy in accurately identifying edges within images. For this paper, due to the long-term memory, nonlocality, and weak singularity of fractional calculus, fractional-order ant colony algorithm combined with fractional differential mask and coefficient of variation (FACAFCV) for image edge detection is proposed. If we set the order of the fractional-order ant colony algorithm and fractional differential mask to ν = 0 , the edge detection method we propose becomes an integer-order edge detection method. We conduct experiments on images that are corrupted by multiplicative noise, as well as on an edge detection dataset. Our experimental results demonstrate that our method is able to detect image edges, while also mitigating the impact of multiplicative noise. These results indicate that our method has the potential to be a valuable tool for edge detection in practical applications.

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Section: Fractional Calculusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Edge Detection Based on Fractional-Order Ant Colony Algorithm

Liu

2023

Fractal Fract

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“…While gray-level textures have a well-documented use in the analysis of biomedical imagery, they also have wider application in analysis of all grayscale images, and have found a wide range of uses, from analysis of landscape images, to material processing. Recent studies have used GLCM in gray-level texture analysis of geological properties [89], in the classification of pork and beef images [90], analysis of asphalt pavement used in roads [91], fingerprint identification [92], and the microstructure of materials and their properties [93]. The range of different applications of gray-level texture analysis, and its use in providing information to machine learning, deep learning and other learning models suggest that it is a robust and useful technique.…”

Section: Gray-level Textures Have Been Used In the Automated Classifi...mentioning

confidence: 99%

Characterization and Classification of Fibrillar Collagen Networks using Gray-Level Texture Analysis

Poole¹

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Biological tissues are a combination of cellular and acellular components. The extracellular matrix (ECM), or the acellular component of tissues, is often overlooked.Tracking changes in the ECM grants insight into how aging, diseases and treatments might affect tissue structures and cellular responses. Using gray-level texture analysis, we analyzed the architecture of fibrillar collagen, a major component of the ECM.Several gray-level textural features were extracted from second harmonic generation (SHG) images and used to characterize collagen network arrangements. This work provides a basis for a classification model aimed to track changes in the cellular microenvironment. This information is vital if we wish to continue exploration of how diseases develop in different tissues, and develop novel and effective treatments for different types of chronic diseases.Foremost, and most certainly, I would like to extend my deepest and sincere gratitude to my supervisor Dr. Leila Mostaço-Guidolin. Her guidance, effort, and determination throughout the course of my studies, and in the writing of this thesis, have been invaluable. Especially during this period of history we find ourselves: the COVID-19 Pandemic, where studies felt like an uphill battle. She helped me ride out the storm, and all the while I gained valuable experience and knowledge that I'm sure will be helpful in the bright future. More so than that, she has been a source of inspiration, in her work ethic, dedication to her profession, and seemingly tireless ability to find time for her students in the midst of her own busy schedule.While I may not have met them, or know them, as much as I would have liked to due to the restrictions of the pandemic, I would like to thank my lab group, the BioImaging and Tissue Eng Lab. From the rare, and very much enjoyable, in-person meeting, to the mid-week Zoom meetings, it was always a pleasure to connect. I would especially like to thank Natasha Kunchur for her advice while I was writing this thesis. I would like to extend my thanks to Carleton University as a whole, an institution I have grown to know rather well in the seven years I have spent studying within iii its walls (two from the comfort of home), for its financial support during this degree.To my friends, for their constant support during my studies, which once appeared like they would never finish. At the times where overwhelming stress and anxiety became too much to bear, they would always be there to provide an ear to hear my complaints, a shoulder to cry on, or provide a much needed distraction.Finally, to my family, without whose support this thesis would never have been completed. Words cannot express how much gratitude I have for your support, guidance and understanding over the last few years. Especially my parents, Christopher Poole and Judith Poole, to whom I dedicate this thesis.

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