This Article 
 Bibliographic References 
 Add to: 
Recognition of Articulated and Occluded Objects
July 1999 (vol. 21 no. 7)
pp. 603-613

Abstract—A model-based automatic target recognition (ATR) system is developed to recognize articulated and occluded objects in Synthetic Aperture Radar (SAR) images, based on invariant features of the objects. Characteristics of SAR target image scattering centers, azimuth variation, and articulation invariants are presented. The basic elements of the new recognition system are described and performance results are given for articulated, occluded and occluded articulated objects and they are related to the target articulation invariance and percent unoccluded.

[1] D. Andersch, S. Lee, H. Ling, and C. Yu, “XPATCH: A High Frequency Electromagnetic Scattering Prediction Code Using Shooting and Bouncing Ray,” Proc. Ground Target Modeling and Validation Conf., pp. 498-507, Aug. 1994.
[2] A. Beinglass and H. Wolfson, “Articulated Object Recognition, or: How to Generalize the Generalized Hough Transform,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 461-466, June 1991.
[3] B. Bhanu, D. Dudgeon, E. Zelnio, A. Rosenfeld, D. Casasent, and I. Reed, “Introduction to the Special Issue on Automatic Target Detection and Recognition,” IEEE Trans. Image Processing, vol. 6, no. 1, pp. 1-6, Jan. 1997,
[4] D. Casasent and S. Ashizawa, “SAR Detection and Recognition Filters,” Optical Eng., pp. 2,729, Oct. 1997.
[5] D. Dudgeon, R. Lacoss, C. Lazott, and J. Verly, “Use of Persistent Scatterers for Model-Based Recognition,” SPIE Proc. Algorithms for Synthetic Aperture Radar Imagery, vol. 2,230, pp. 356-368, Orlando, Fla., Apr. 1994.
[6] W.E.L. Grimson, Object Recognition by Computer. MIT Press, 1990.
[7] Y. Hel-Or and M. Werman, “Recognition and Localization of Articulated Objects,” Proc. IEEE Workshop Motion of Non-Rigid and Articulated Objects, pp. 116-123, Austin, Texas, 1994.
[8] K. Ikeuchi, T. Shakunga, M. Wheeler, and T. Yamazaki, “Invariant Histograms and Deformable Template Matching for SAR Target Recognition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 100-105, June 1996.
[9] Y. Lamdan and H.J. Wolfson, "Geometric hashing: A general and efficient model-based recognition scheme," Second Int'l Conf. Computer Vision, pp. 238-249, 1988.
[10] L. Novak, G. Owirka, and C. Netishen, “Performance of a High-Resolution Polarimetric SAR Automatic Target Recognition System,” The Lincoln Laboratory J., vol. 6. no. 1, pp. 11-24, 1993.
[11] J. Verly, R. Delanoy, and C. Lazott, “Principles and Evaluation of an Automatic Target Recognition System for Synthetic Aperture Radar Imagery Based on the Use of Functional Templates,” SPIE Proc. Automatic Target Recognition III, vol. 1,960, pp. 57-71, Orlando, Fla., Apr. 1993.
[12] D. Wehner, High Resolution Radar, Second ed. Boston: Artech House, 1995.
[13] J.H. Yi, B. Bhanu, and M. Li, “Target Indexing in SAR Images Using Scattering Centers and the Hausdorff Distance,” Pattern Recognition Letters, vol. 17, pp. 1,191-1,198, 1996.

Index Terms:
Articulation invariance, automatic target recognition, azimuth variance, empirical performance modeling, geometric hashing, synthetic aperture radar images.
Grinnell Jones, Bir Bhanu, "Recognition of Articulated and Occluded Objects," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 7, pp. 603-613, July 1999, doi:10.1109/34.777371
Usage of this product signifies your acceptance of the Terms of Use.