Comparative Study of Supervised Machine Learning Color-Based Segmentation for Object Detection in X-Ray Baggage Images for Intelligent Transportation Systems

Chouai, Mohamed; Merah, Mostefa; Sancho‐Gómez, José‐Luis; Mimi, Malika

doi:10.1007/978-3-030-53440-0_11

Cited by 2 publications

(8 citation statements)

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“…Some studies on automatic X-ray security data analysis applied image segmentation. The work in [84] presented an automatic segmentation method for security screening that first enhances images to improve performance and then applies color-based pixel segmentation to distinguish diverse materials (organic, inorganic, mixed, and opaque objects) from the background. A method using a DL model as a robust feature extractor and an adversarial auto-encoder to classify images into organic and inorganic classes considering the overlap among the materials was proposed in [3].…”

Section: Securitymentioning

confidence: 99%

“…MLPs have been used in several X-ray analysis works including [32], [51], [60], [84], [94]. In [32], [51], [60], they were used for welding defect analysis, in [94] to evaluate synthesized data, and in [84] they were compared against other traditional classifier types in baggage image segmentation.…”

Section: ) Traditional Classifiersmentioning

confidence: 99%

“…In X-ray image processing, SVMs have been used in [41], [53], [57], [59], [67], [69], [84], [96], [108] as the classifier. In [41], both linear and RBF SVMs were used for classifying patches of cast images (defect vs. no defect).…”

Section: ) Traditional Classifiersmentioning

confidence: 99%

“…Binary classification with different kernels was applied in [53], [59], whereas [57] applied multiclass SVMs using both one vs. one or one vs. all approaches. In [84], three SVM models with different kernels (linear, RBF, polynomial) were used to segment X-ray baggage security images into organic and inorganic material. In [69], [108], SVMs were used for binary classification of security images (gun or no).…”

Section: ) Traditional Classifiersmentioning

confidence: 99%

“…Random forest (RF) applies bootstrap aggregation to build multiple classification trees and then classifies the objects based on the majority vote of the trees. RF was compared against other traditional classifiers in [84] for an X-ray baggage security segmentation, but it showed inferior performance. In [115], RF was compared against k-nearest neighbors classifier in an imbalanced mineral phase segmentation task and the algorithms achieved similar performance.…”

Section: ) Traditional Classifiersmentioning

confidence: 99%

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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%