Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
A New Enhancement Technique of X-Ray Carry-on Luggage Images Based on DWT and Fuzzy Theory
ICCSIT '08 Proceedings of the 2008 International Conference on Computer Science and Information Technology
BRIEF: binary robust independent elementary features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Image enhancement optimization for hand-luggage screening at airports
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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X-ray inspection systems play a crucial role in security checkpoints, especially at the airports. Automatic analysis of X-ray images is desirable for reducing the workload of the screeners, increasing the inspection speed and for privacy concerns. X-ray images are quite different from visible spectrum images in terms of signal content, noise and clutter. This different type of data has not been sufficiently explored by computer vision researchers, due probably to the unavailability of such data. In this paper, we investigate the applicability of bag of visual words (BoW) methods to the classification and retrieval of X-ray images. We present the results of extensive experiments using different local feature detectors and descriptors. We conclude that although the straightforward application of BoW on X-ray images does not perform as well as it does on regular images, the performance can be significantly improved by utilizing the extra information available in X-ray images.