Object Recognition in X-ray Testing Using Adaptive Sparse Representations

Mery, Domingo; Svec, Erick; Arias, Marco

doi:10.1007/s10921-016-0362-8

Cited by 21 publications

(11 citation statements)

References 24 publications

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“…A multistage analyzer and classifier system was proposed in [67] to automatically perform threat recognition in different security monitoring environments to identify a wide range of firearm threats. To manage intraclass variability in contrast, pose, image size, and focal distance, a new representation approach was introduced in [68] to recognize objects. In [69], a method was adopted for binary classification of firearms versus other objects in baggage security X-ray images.…”

Section: Securitymentioning

confidence: 99%

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learningbased object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

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Section: Securitymentioning

confidence: 99%

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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“…Similar works [37,78,79,83,107] exhaustively evaluate various computer vision techniques, with a specific focus on k-nn based sparse representation. A kmeans algorithm [102] clusters the features, segmented from input via an adaptive k-means [108] and extracted via SIFT [100].…”

Section: Object Classificationmentioning

confidence: 99%

Towards automatic threat detection: A survey of advances of deep learning within X-ray security imaging

Akçay

Breckon

2022

Pattern Recognition

102

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“…In [ 4 ], a method using visual vocabulary and an occurrence structure was proposed to detect three different prohibited items. In addition, an approach called adaptive sparse representation (XASR+) [ 5 ] was proposed to recognize items automatically in cases with less constrained conditions including contrast variations, pose variations, image size variations, and focal distance variations. In the case of the dual-energy X-ray image, Riffo and Mery [ 6 ] proposed an active X-ray testing framework that is able to find an adequate view of the object item to detect razor blades in different cases.…”

Section: Introductionmentioning

confidence: 99%

A Data Augmentation Method for Prohibited Item X-Ray Pseudocolor Images in X-Ray Security Inspection Based on Wasserstein Generative Adversarial Network and Spatial-and-Channel Attention Block

Liu

Pei-xin

et al. 2022

Computational Intelligence and Neuroscience

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For public security and crime prevention, the detection of prohibited items in X-ray security inspection based on deep learning has attracted widespread attention. However, the pseudocolor image dataset is scarce due to security, which brings an enormous challenge to the detection of prohibited items in X-ray security inspection. In this paper, a data augmentation method for prohibited item X-ray pseudocolor images in X-ray security inspection is proposed. Firstly, we design a framework of our method to achieve the dataset augmentation using the datasets with and without prohibited items. Secondly, in the framework, we design a spatial-and-channel attention block and a new base block to compose our X-ray Wasserstein generative adversarial network model with gradient penalty. The model directly generates high-quality dual-energy X-ray data instead of pseudocolor images. Thirdly, we design a composite strategy to composite the generated and real dual-energy X-ray data with background data into a new X-ray pseudocolor image, which can simulate the real overlapping relationship among items. Finally, two object detection models with and without our data augmentation method are applied to verify the effectiveness of our method. The experimental results demonstrate that our method can achieve the data augmentation for prohibited item X-ray pseudocolor images in X-ray security inspection effectively.

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Object Recognition in X-ray Testing Using Adaptive Sparse Representations

Cited by 21 publications

References 24 publications

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Towards automatic threat detection: A survey of advances of deep learning within X-ray security imaging

A Data Augmentation Method for Prohibited Item X-Ray Pseudocolor Images in X-Ray Security Inspection Based on Wasserstein Generative Adversarial Network and Spatial-and-Channel Attention Block

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