Geometry-based Completed Local Binary Pattern for Texture Image Classification

Wang, Wei; Kou, Qiqi; Zhou, Shu; Luo, Ke; Zhang, Lifeng

doi:10.1109/icicsp50920.2020.9232056

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…At present, research on the authenticity identification of anti-counterfeiting code mainly focuses on traditional image processing and machine learning algorithms. To evaluate the classification performance of the FG-DPANet network, the 13 most common and advanced existing methods, CLBC [49], CLBP [50], COV-LBPD [51], ECLBP [22], LBP [52], LDEP [53], LGONBP [23], MCDR [54], MRELBP [55], RALBGC [56], RAMBP [24], MMLR [57] and GCLBP [58] are chosen for comparison to the FG-DPANet network. To comprehensively evaluate the performance of the digital image anti-counterfeiting algorithm, we test the classification accuracy, recall and F1 value of each digital image anti-counterfeiting algorithm.…”

Section: Compared To the Textural Anti-counterfeiting Algorithmsmentioning

confidence: 99%

Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

You¹,

Zheng²,

Guo³

et al. 2023

Computers, Materials &Amp; Continua

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The authenticity identification of anti-counterfeiting codes based on mobile phone platforms is affected by lighting environment, photographing habits, camera resolution and other factors, resulting in poor collection quality of anti-counterfeiting codes and weak differentiation of anti-counterfeiting codes for high-quality counterfeits. Developing an anticounterfeiting code authentication algorithm based on mobile phones is of great commercial value. Although the existing algorithms developed based on special equipment can effectively identify forged anti-counterfeiting codes, the anti-counterfeiting code identification scheme based on mobile phones is still in its infancy. To address the small differences in texture features, low response speed and excessively large deep learning models used in mobile phone anti-counterfeiting and identification scenarios, we propose a feature-guided double pool attention network (FG-DPANet) to solve the reprinting forgery problem of printing anti-counterfeiting codes. To address the slight differences in texture features in high-quality reprinted anti-counterfeiting codes, we propose a feature guidance algorithm that creatively combines the texture features and the inherent noise feature of the scanner and printer introduced in the reprinting process to identify anti-counterfeiting code authenticity. The introduction of noise features effectively makes up for the small texture difference of high-quality anti-counterfeiting codes. The double pool attention network (DPANet) is a lightweight double pool attention residual network. Under the condition of ensuring detection accuracy, DPANet can simplify the network structure as much as possible, improve the network reasoning speed, and run better on mobile devices with low computing power. We conducted a series of experiments to evaluate the FG-DPANet proposed in this paper. Experimental results show that the proposed FG-DPANet can resist highquality and small-size anti-counterfeiting code reprint forgery. By comparing with the existing algorithm based on texture, it is shown that the proposed method has a higher authentication accuracy. Last but not least, the proposed scheme has been evaluated in the anti-counterfeiting code blurring scene, and the results show that our proposed method can well resist slight blurring of anti-counterfeiting images.

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Section: Compared To the Textural Anti-counterfeiting Algorithmsmentioning

confidence: 99%

Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

You¹,

Zheng²,

Guo³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Due to its classification accuracy and computer simplicity, LBP Texture Operator has been a significant approach in numerous applications. e methodology to the usually diverse quantitative and organizational model of image segmentation might be considered as a unifying one [30]. Maybe the most essential quality in real-world applications of the LBP operator is its stability against monotonous grey-scale shifts produced by lighting difference are calculated by neighboring pixels in the x-direction; then, both the y address sum and difference are reproduced in the y-direction [31].…”

Section: Feature Extractionmentioning

confidence: 99%

The Use of Different Image Recognition Techniques in Food Safety: A Study

Khan¹,

Kumar

Dhingra³

et al. 2021

Journal of Food Quality

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Food safety refers to preparing, transporting, and storing food to avoid foodborne sickness and harm. From farm to factory and factory to fork, food items may meet various health dangers. Therefore, food safety is crucial both monetarily and morally. The implications of failing to comply with food safety requirements are varied. The requirement for accurate, quick, and nonpartisan quality assessments of these features in food products continues to rise with increased demands for dietary materials and high-quality requirements. Computer vision provides an automatic, nondestructive, and economic approach to achieving these aims. A substantial research has demonstrated its effectiveness for fruit and vegetable assessment and classification. It stresses the critical components of image processing technology and a survey of the most current advances across the food sector. This article outlines the essential parts of a computer vision system. In order to avoid foodborne disease and ensure food security, fast and effective detection of pathogenic microorganisms is crucial for public safety biomonitoring. Over the years, microorganism detection techniques have evolved.

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Robust local binary pattern for face recognition in different challenges

Karanwal

2022

Multimed Tools Appl

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Geometry-based Completed Local Binary Pattern for Texture Image Classification

Cited by 4 publications

References 21 publications

Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

The Use of Different Image Recognition Techniques in Food Safety: A Study

Robust local binary pattern for face recognition in different challenges

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