Object Recognition in Multi-View Dual Energy X-ray Images

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.

[1]  Peter Bock,et al.  Identification of Objects-of-Interest in X-Ray Images , 2006, 35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06).

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Muhammet Bastan,et al.  Visual Words on Baggage X-Ray Images , 2011, CAIP.

[4]  T. P. Breckon,et al.  Improving feature-based object recognition for X-ray baggage security screening using primed visualwords , 2013, 2013 IEEE International Conference on Industrial Technology (ICIT).

[5]  S. Agaian,et al.  Automatic Detection of Potential Threat Objects in X-ray Luggage Scan Images , 2008, 2008 IEEE Conference on Technologies for Homeland Security.

[6]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Stefan Roth,et al.  Object Detection in Multi-view X-Ray Images , 2012, DAGM/OAGM Symposium.

[8]  Najla Megherbi Bouallagu,et al.  A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery , 2013, Pattern Recognit..

[9]  Cordelia Schmid,et al.  A sparse texture representation using local affine regions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Dongmei Liu,et al.  A united classification system of X-ray image based on fuzzy rule and neural networks , 2008, 2008 3rd International Conference on Intelligent System and Knowledge Engineering.

[11]  Domingo Mery,et al.  Automated detection in complex objects using a tracking algorithm in multiple X-ray views , 2011, CVPR 2011 WORKSHOPS.

[12]  Christoph H. Lampert,et al.  Learning to Localize Objects with Structured Output Regression , 2008, ECCV.

[13]  Thomas M. Breuel,et al.  Visual cortex inspired features for object detection in X-ray images , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[14]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Christoph H. Lampert,et al.  Efficient Subwindow Search: A Branch and Bound Framework for Object Localization , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Thomas Hofmann,et al.  Support vector machine learning for interdependent and structured output spaces , 2004, ICML.

[17]  Joost van de Weijer,et al.  Boosting color saliency in image feature detection , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Thorsten Joachims,et al.  Cutting-plane training of structural SVMs , 2009, Machine Learning.

[19]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.