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One of the most important applications of computer vision algorithms is the detection of threat objects in x-ray security images. However, in the practical setting, this task is complicated by two properties inherent to the dataset, namely, the problem of class imbalance and visual complexity. In our previous work, we resolved the class imbalance problem by using a GAN-based anomaly detection to balance out the bias induced by training a classification model on a non-practical dataset. In this paper, we propose a new method to alleviate the visual complexity problem by using a KNN-based automatic cropping algorithm to remove distracting and irrelevant information from the x-ray images. We use the cropped images as inputs to our current model. Empirical results show substantial improvement to our model, e.g. about 3% in the practical dataset, thus further outperforming previous approaches, which is very critical for security-based applications.
목차
Abstract
Ⅰ. Introduction
Ⅱ. Proposed Method
Ⅲ. Experiment Results
Ⅳ. Conclusion
References
참고문헌 (16)
참고문헌 신청
Vladimir Riffo / 2016 / Automated Detection of Threat Objects Using Adapted Implicit Shape Model / IEEE Transactions on Systems, Man, and Cybernetics: Systems 46(4):472~482
Domingo Mery / 2016 / Object Recognition in X-ray Testing Using Adaptive Sparse Representations / Journal of Nondestructive Evaluation 35(3):9
Domingo Mery / 2017 / Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection / IEEE Transactions on Systems, Man, and Cybernetics: Systems 47(4):682~692
Samet Akcay / 2018 / Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery / IEEE Transactions on Information Forensics and Security 13(9):2203~2215
Domingo Mery / 2015 / GDXray: The Database of X-ray Images for Nondestructive Testing / Journal of Nondestructive Evaluation 34(4)
Domingo Mery / 2016 / Object Recognition in X-ray Testing Using Adaptive Sparse Representations / Journal of Nondestructive Evaluation 35(3):9
Domingo Mery / 2017 / Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection / IEEE Transactions on Systems, Man, and Cybernetics: Systems 47(4):682~692
Samet Akcay / 2018 / Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery / IEEE Transactions on Information Forensics and Security 13(9):2203~2215
Domingo Mery / 2015 / GDXray: The Database of X-ray Images for Nondestructive Testing / Journal of Nondestructive Evaluation 34(4)
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UCI(KEPA) : I410-ECN-0101-2020-056-000378630