Efficient, accurate and fast pupil segmentation for pupillary boundary in iris recognition

Jamaludin, Shahrizan; Ayob, Ahmad Faisal Mohamad; Akhbar, Mohd Faizal Ali; Ali, Ahmad Ali Imran Mohd; Imran, Md Mahadi Hasan; Norzeli, Syamimi Mohd; Mohamed, Saiful Bahri

doi:10.1016/j.advengsoft.2022.103352

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…Microbiological corrosion is common in sewage pipes where acids are formed from chemical compounds and acidophilic bacterial colonies. The computer vision and image processing techniques can complement chemical approaches for microbiological corrosion assessment, detection, and maintenance [110].…”

Section: Discussion and Future Recommendationmentioning

confidence: 99%

Computer Vision and Image Processing Approaches for Corrosion Detection

Ali,

Jamaludin,

Imran

et al. 2023

JMSE

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Corrosion is an undesirable phenomenon resulting in material deterioration and degradation through electrochemical or chemical reactions with the surrounding environment. Additionally, corrosion presents considerable threats in both the short and long term because of its ability to create failures, leakages, and damage to materials, equipment, and environment. Despite swift technological developments, it remains difficult to determine the degrees of corrosion due to the different textures and the edgeless boundary of corrosion surfaces. Hence, there is a need to investigate the robust corrosion detection algorithms that are suitable for all degrees of corrosion. Recently, many computer vision and image processing algorithms have been developed for corrosion prediction, assessment, and detection, such as filtering, texture, color, pixelation, image enhancement, wavelet transformation, segmentation, classification, and clustering approaches. As a result, this paper reviews and discusses the state-of-the-art computer vision and image processing methods that have been developed for corrosion detection in various applications, industries, and academic research. The challenges for corrosion detection using computer vision and image processing algorithms are also explored. Finally, recommendations for future research are also detailed.

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Section: Discussion and Future Recommendationmentioning

confidence: 99%

Computer Vision and Image Processing Approaches for Corrosion Detection

Ali,

Jamaludin,

Imran

et al. 2023

JMSE

Self Cite

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“…Supervised Reinforcement Learning is a machine learning algorithm that allows the machine or software agents to intelligently regulate the best behavior within a definite context and exploit the performance. RL models are enforced to learn optimum objectives through the trial-and-error method [77]. As shown in Figure 7, the RL paradigm enables an agent to remember by investigating the potential actions and modifying its behaviors in response to the reward.…”

Section: Elementmentioning

confidence: 99%

Application of Artificial Intelligence in Marine Corrosion Prediction and Detection

Imran

Jamaludin

Ayob

et al. 2023

JMSE

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One of the biggest problems the maritime industry is currently experiencing is corrosion, resulting in short and long-term damages. Early prediction and proper corrosion monitoring can reduce economic losses. Traditional approaches used in corrosion prediction and detection are time-consuming and challenging to execute in inaccessible areas. Due to these reasons, artificial intelligence-based algorithms have become the most popular tools for researchers. This study discusses state-of-the-art artificial intelligence (AI) methods for marine-related corrosion prediction and detection: (1) predictive maintenance approaches and (2) computer vision and image processing approaches. Furthermore, a brief description of AI is described. The outcomes of this review will bring forward new knowledge about AI and the development of prediction models which can avoid unexpected failures during corrosion detection and maintenance. Moreover, it will expand the understanding of computer vision and image processing approaches for accurately detecting corrosion in images and videos.

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“…Iris recognition is a biometric system that identifies an individual based on the iris tissue in the human eye [ 28 ]. Iris fiber tissue is very intricate.…”

Section: Biometric Recognition Mechanismmentioning

confidence: 99%

A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications

Niu

et al. 2023

Micromachines

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Identification technology based on biometrics is a branch of research that employs the unique individual traits of humans to authenticate identity, which is the most secure method of identification based on its exceptional high dependability and stability of human biometrics. Common biometric identifiers include fingerprints, irises, and facial sounds, among others. In the realm of biometric recognition, fingerprint recognition has gained success with its convenient operation and fast identif ication speed. Different fingerprint collecting techniques, which supply fingerprint information for fingerprint identification systems, have attracted a significant deal of interest in authentication technology regarding fingerprint identification systems. This work presents several fingerprint acquisition techniques, such as optical capacitive and ultrasonic, and analyzes acquisition types and structures. In addition, the pros and drawbacks of various sensor types, as well as the limits and benefits of optical, capacitive, and ultrasonic kinds, are discussed. It is the necessary stage for the application of the Internet of Things (IoT).

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Efficient, accurate and fast pupil segmentation for pupillary boundary in iris recognition

Cited by 10 publications

References 31 publications

Computer Vision and Image Processing Approaches for Corrosion Detection

Computer Vision and Image Processing Approaches for Corrosion Detection

Application of Artificial Intelligence in Marine Corrosion Prediction and Detection

A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications

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