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Color View Synthesis For Animated Depth Security X-Ray Imaging

Authors: O. Abusaeeda; J. P. O Evans; D. Downes;

Color View Synthesis For Animated Depth Security X-Ray Imaging

Abstract

{"references": ["J.P.O. Evans, \"Stereoscopic imaging using folded linear dual-energy\nX-ray detectors,\" Institute of Physics (IOP) Journal Meas. Sc. & Tech,\nvol. 13, no. 9, pp. 1388-1397, 2002.", "J.P.O. Evans, M. Robinson, and S.X. Godber, \"A new stereoscopic\nX-ray imaging technique using a single X-ray source: theoretical\nanalysis,\" Non-Destructive Testing and Evaluation Int. (NDT&E), vol.\n29, no. 1, pp. 27-35, 1996.", "J.P.O. Evans, and M. Robinson, \"Design of a stereoscopic X-ray\nimaging system using a single X-ray source,\" Non-Destructive Testing\nand Evaluation Int. (NDT&E), vol. 33, no. 5, pp. 325-332, 2000.", "J.P.O. Evans and H. W. Hon, \"Dynamic stereoscopic X-ray imaging,\" J.\nof NDT&E, vol. 35, no. 5, pp. 337-345, 2002.", "J.P.O. Evans, Y. Liu and J. W. Chan, \"Depth from motion 3D X-ray\nimaging for security screening,\" in IEE ICDP, pp. 5-8, 2005.", "J.P.O. Evans, Y. Liu, J. W. Chan, and D. Downes, \"View synthesis for\ndepth from motion 3D X-ray imaging,\" Pattern Recognit. Lett, vol. 27,\nno. 15, pp. 1863-1873, 2006.", "D.G. Lowe, \"Distinctive image features from scale-invariant keypoints,\"\nInternational Journal of Computer Vision, vol. 60, no. 2, pp. 91-110,\n2004.", "H. Moravec, \"Rover visual obstacle avoidance,\" International Joint\nConference on Artificial Intelligence Vancouver, Canada, pp. 785-790,\n1981.", "C. Harris, \"Geometry from visual motion,\" Active vision, MIT Press, pp.\n263- 284, 1993.\n[10] Z. Zhang, R. Deriche, O.Faugeras, and Q.T. Luong, \"A robust technique\nfor matching two uncalibrated images through the recovery of the\nunknown epipolar geometry,\" Artificial Intelligence, vol. 78, no 1-2,\npp. 87-119, 1995.\n[11] K. Mikolajczyk, and C. Schmid, \"An affine invariant interest point\ndetector,\" European Conference on Computer Vision, pp. 128-142,\n2002.\n[12] M. Brown, and D.G. Lowe, \"Invariant features from interest point\ngroups,\" British Machine Vision Conference, Cardiff, Wales, Citeseer,\npp. 656-665, 2002.\n[13] Y. Ke, and R. Sukthankar, \"PCA-SIFT: A more distinctive\nrepresentation for local image descriptors,\" Conference on Computer\nVision and Pattern Recognition, Washington, USA, pp. 511-517, 2004.\n[14] L. Juan, and O. Gwon, \"A Comparison of SIFT, PCA-SIFT and SURF,\"\nInternational Journal of Image Processing (IJIP), vol. 3, no. 4, pp.\n143-152, 2009.\n[15] H. Bay, T. Tuytelaars, and L. Van Gool, \"Speeded-up robust features\n(SURF),\" Computer Vision and Image Understanding, vol. 110, no. 3,\npp. 346-359, 2008.\n[16] Z. Feng, B.Yang, Y.Chen, Y. Zheng, and T. Xu, \"Features extraction\nfrom hand images based on new detection operators,\" Pattern\nRecognition, vol. 44, no. 5, pp. 1089-1105, 2010.\n[17] J. W. Chan O. Abusaeeda, J.P.O. Evans, D. Downes, X. Wang and\nL.Yong, \"Feasibility of SIFT to Synthesise KDEX Imagery for Aviation\nLuggage Security Screening,\" IET The Crime and Security Conference:\nImaging for Crime Detection and Prevention, ISBN 978-1-84919-207-1,\n2009.\n[18] S. Seitz, and C. Dyer, \"View morphing,\" the 23rd annual conference on\nComputer graphics and interactive techniques ACM SIGGRAPH,\npp. 21-30 1996.\n[19] E. Shechtman, A. Rav-Acha, M. Irani, and S.M. Seitz, \"Regenerative\nmorphing,\" IEEE computer society conferene on Computer Vision and\nPattern Recognition, San Francisco, CA, USA, pp. 1-8, 2010.\n[20] B. Zitova, and J. Flusser, \"Image registration methods: a survey,\" Image\nVision Comput, vol. 21, no. 11, pp. 977-1000, 2003.\n[21] C.A.Glasbey, and K.V. Mardia, \"A review of image-warping methods,\"\nJournal of applied statistics, vol. 25, no. 2, pp. 155-172, 1998.\n[22] G. Wolberg, \"Image morphing: a survey,\" The Visual Computer, vol. 14,\nno. 8, pp. 360-372, 1998.\n[23] Z. Zhang, L. Wang, B. Guo, and H.Y. Shum, \"Feature-based light field\nmorphing,\" ACM Transactions on Graphics, vol. 21, no. 3, pp. 457-464,\n2002.\n[24] R. Manning, and C. Dyer, \"Interpolating view and scene motion by\ndynamic view morphing,\" Proc. CVPR, pp. 388-394, 1999.\n[25] J.P.O. Evans, Y. Liu and J. W. Chan, \"Depth from motion 3D X-ray\nimaging for security screening,\" Conference, in IEE ICDP, pp. 5-8,\n2005.\n[26] Wang T. W. and Evans J. P. O., \"Stereoscopic dual-energy x-ray\nimaging for target materials identification\", Conference, in IEE\nProceedings Vision Image Processing, Vol. 150, no. 2, pp. 122-130,\nApril 2003.\n[27] J. Gomes, L. Darsa, B. Costa, and L. Velho, \"Warping and Morphing of\nGraphical Objects,\" Morgan Kaufmann, San Francisco, Calif., (1998).\n[28] Z. Zhang, L. Wang, B. Guo, and H.Y. Shum, \"Feature-based light field\nmorphing,\" ACM Transactions on Graphics,Vol. 21, no. 3, pp. 457-464,\n2002."]}

We demonstrate the synthesis of intermediary views within a sequence of color encoded, materials discriminating, X-ray images that exhibit animated depth in a visual display. During the image acquisition process, the requirement for a linear X-ray detector array is replaced by synthetic image. Scale Invariant Feature Transform, SIFT, in combination with material segmented morphing is employed to produce synthetic imagery. A quantitative analysis of the feature matching performance of the SIFT is presented along with a comparative study of the synthetic imagery. We show that the total number of matches produced by SIFT reduces as the angular separation between the generating views increases. This effect is accompanied by an increase in the total number of synthetic pixel errors. The trends observed are obtained from 15 different luggage items. This programme of research is in collaboration with the UK Home Office and the US Dept. of Homeland Security.

Keywords

X-ray, view synthesis, KDE, kinetic depth

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