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Object Recognition in Multi-View Dual Energy X-ray Images
(B M V A Press, 2013) Bastan, Muhammet; Byeon, Wonmin; Breuel, Thomas M.
Object recognition in X-ray images is an interesting application of machine vision that can help reduce the workload of human operators of X-ray scanners at security checkpoints. In this paper, we first present a comprehensive evaluation of image classification and object detection in X-ray images using standard local features in a BoW framework with (structural) SVMs. Then, we extend the features to utilize the extra information available in dual energy X-ray images. Finally, we propose a multi-view branch-and-bound algorithm for multi-view object detection. Through extensive experiments on three object categories, we show that the classification and detection performance substantially improves with the extended features and multiple views.