Abstract
This paper presents an approach for recognizing articulated vehicles in Synthetic Aperture Radar (SAR) images based on invariant properties of the objects. Using SAR scattering center locations and magnitudes as features, the invariance of these features with articulation (e.g. turret rotation of a tank) is shown for SAR signatures of actual vehicles from the MSTAR (Public) data. Although related to geometric hashing, our recognition approach is specifically designed for SAR, taking into account the great azimuthal variation and moderate articulation invariance of SAR signatures. We present a recognition system, using scatterer locations and magnitudes, that achieves excellent results with the real SAR targets in the MSTAR data. The articulation invariant properties of the objects are used to characterize recognition system performance in terms of probability of correct identification as a function of percent invariance with articulation. Results are also presented for occluded articulated objects.
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References
Beinglass, A., Wolfson, H.: Articulated object recognition, or: How to generalize the generalized Hough transform. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, June 1991, pp. 461–466 (1991)
Dudgeon, D., Lacoss, R., Lazott, C., Verly, J.: Use of persistent scatterers for model-based recognition. In: SPIE Proceedings: Algorithms for Synthetic Aperture Radar Imagery, April 1994, vol. 2230, pp. 356–368 (1994)
Grimson, W.E.L.: Object Recognition by Computer: The Role of Geometric Constraints. MIT Press, Cambridge (1990)
Hel-Or, Y., Werman, M.: Recognition and localization of articulated objects. In: IEEE Workshop on Motion of Non-Rigid and Articulated Objects, pp. 116–123 (November 1994)
Jones III, G., Bhanu, B.: Recognition of Articulated and Occluded Objects. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(7), 603–613 (1999)
Khoros Pro v2.2 User’s Guide. Addison Wesley Longman Inc. (1998)
Lamden, Y., Wolfson, H.: Geometric hashing: A general and efficient model-based recognition scheme. In: Proc. Int. Conference on Computer Vision, December 1988, pp. 238–249 (1988)
Novak, L., Owirka, G., Netishen, C.: Radar target identification using spatial matched filters. Pattern Recognition 27(4), 607–617 (1994)
Ross, T., Worrell, S., Velten, V., Mossing, J., Bryant, M.: Standard SAR ATR Evaluation Experiments using the MSTAR Public Release Data Set. In: SPIE Proceedings: Algorithms for Synthetic Aperture Radar Imagery V, April 1998, vol. 3370, pp. 566–573 (1998)
Verly, J., Delanoy, R., Lazott, C.: Principles and evaluation of an automatic target recognition system for synthetic aperture radar imagery based on the use of functional templates. In: SPIE Proceedings: Automatic Target Recognition III, April 1993, vol. 1960, pp. 57–71 (1993)
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Jones, G., Bhanu, B. (2000). Recognition of Articulated Objects in SAR Images. In: Nagel, HH., Perales López, F.J. (eds) Articulated Motion and Deformable Objects. AMDO 2000. Lecture Notes in Computer Science, vol 1899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10722604_9
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DOI: https://doi.org/10.1007/10722604_9
Publisher Name: Springer, Berlin, Heidelberg
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