A fast specular reflection removal based on pixels properties method

Jamaludin, Shahrizan; Zainal, Nasharuddin; Zaki, Wan Mimi Diyana Wan

doi:10.11591/eei.v9i6.2524

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…RL is predominantly more efficient in dynamic and not completely deterministic environment RL [68]. Recent research discloses that RL has been applied in many different fields, such as speech recognition [60,61], [78][79][80][81], computational biology [82][83][84], computational finance [85][86][87], computer vision, and image processing [88][89][90][91][92][93], energy production [94,95]. Various field methods have been adopted for this purpose [96,97].…”

Section: Elementmentioning

confidence: 99%

“…Therefore, developing other methods for corrosion prediction and detection is essential. Consequently, new ways of artificial intelligence have been able to attract researchers because of enormous advantages over human intelligence [18,19], advanced technology to solve complex problems [20][21][22], faster decision making [23] such as big data, machine learning (ML), deep learning (DL), neural networks (NN), face recognition, pattern recognition, image classification, and recognition, character recognition [24][25][26][27][28]. Cost reductions and risk reductions have been the driving forces for research into automated corrosion detection over the past ten years [29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Elementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Marine Corrosion Prediction and Detection

Imran

Jamaludin

Ayob

et al. 2023

JMSE

Self Cite

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“…Feature extraction is the fourth process, and it involves extracting corrosion characteristics from the image that has been segmented [13,14]. Corrosion features are retrieved to obtain the most significant information from an image.…”

Section: Feature Extractionmentioning

confidence: 99%

Computer Vision and Image Processing Approaches for Corrosion Detection

Ali,

Jamaludin,

Imran

et al. 2023

JMSE

Self Cite

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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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“…These reflection artefacts are white-dot areas that can introduce noisy information and reduce the efficiency of visual computing system [50]. In particular, following the indications provided in [21], the boundaries of these artefacts were detected, and then an interpolation operation was performed in order to fill the white area (Figure 4e). These pre-processed iris images (2,250) of 100 different users represent our initial dataset, and they are used as the inputs for our analysis.…”

Section: Ubiris-v2 Irisseg-epmentioning

confidence: 99%

Verification system based on long-range iris and Graph Siamese Neural Networks

Zola

Fernandez-Carrasco

Bruse

et al. 2022

2022 the 3rd European Symposium on Software Engineering

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Biometric systems represent valid solutions in tasks like user authentication and verification. However, especially when physical biometrics are used, as is the case of iris recognition, they require specific hardware such as retina scanners, sensors, or HD cameras to achieve relevant results. In this sense, the majority of the prior studies propose to improve the acquisitions of long-range (LR) iris images using specific technologies, without trying to analyze their potential during the processing and classification phase. For this reason, in this work, we propose a novel approach that uses LR distance images taken from traditional cameras for implementing an iris verification system. More specifically, we present a novel methodology for converting LR iris images into graphs based on 1) converting the image in greyscale, 2) discretizing it and 3) performing a binarization, then, 4) graph nodes are defined by detecting the connected components in the binarized image, as well as their 5) features and 6) their edges. Once these graphs are extracted, they are used as input in Graph Siamese Neural Networks (GSNN) to predict whether two graphs belong to the same person. In this study, three experiments are carried out. In the first one, we evaluate how the number of nodes in the generated graphs affects the verification system. In the second one, specific filters are used to enhance the spectral information of the LR images, changing their graph definition. Finally, in the third one, we evaluate how the number of users impacts the verification system performance.Results show that a small number of nodes or using too many nodes worsens the model performance, whereas it is possible to improve the F1-score of the GSNN using spectral enhanced images (up to 77%). Furthermore, increasing the number of users shows a small deterioration in the GSNNs performance. These results demonstrate the suitability of this approach, encouraging the community to explore the usage of traditional LR distance images and graph application in biometric systems. CCS Concepts: • Theory of computation → Graph algorithms analysis; • Security and privacy → Biometrics; • Computing methodologies → Spectral methods; Image processing.

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A fast specular reflection removal based on pixels properties method

Cited by 5 publications

References 19 publications

Application of Artificial Intelligence in Marine Corrosion Prediction and Detection

Application of Artificial Intelligence in Marine Corrosion Prediction and Detection

Computer Vision and Image Processing Approaches for Corrosion Detection

Verification system based on long-range iris and Graph Siamese Neural Networks

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