This Article 
 Bibliographic References 
 Add to: 
Object Identification From Multiple Images Based on Point Matching Under a General Transformation
July 1994 (vol. 16 no. 7)
pp. 751-756

This work is motivated by ship identification from a sequence of ISAR images. Maximum likelihood classification, based on point matching, is formulated when the observed images are subject to missing points and phantoms. The 3-D to 2-D transformation is assumed to be known only in a certain parametric form. Proper weights, based on the noise levels for all images, are derived for the classification formula. The new formulation simplifies the computation of matching and makes its extension to object identification from multiple images feasible. Moreover, some theoretical properties of the identification procedure can now be investigated. Guidelines on which groups of objects are easier to distinguish are found from statistical theory followed by intuitive explanation. This method is then applied to ship identification with simulated ISAR images.

[1] D. Lavine, B. A. Lambird, and L. N. Kanal, "Recognition of spatial point patterns,"Pattern Recognit., vol. 16, 289-295, 1983.
[2] H. S. Baird,Model-Based Image Matching Using Location, Cambridge, MA: MIT Press, 1986.
[3] P. M. Griffin and C. Alexopoulos, "Point pattern matching using centroid bounding,"IEEE Trans. Syst. Man. Cybern., vol. SMC-19, 1274-1276, 1989.
[4] T. E. Flick and L. K. Jones, "A combinational approach for classification of patterns with missing information and random orientation,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-8, 482-490, 1986.
[5] M. C. K. Yang and J. S. Lee, "Statistical theory of object identification from multiple images," Tech. Rep. 432, Dep. of Statist., Univ. of Florida, 1993.
[6] R. J. Serflling,Approximation Theorems of Mathematical Statistics. New York: Wiley, 1980.
[7] J. S. Lee and I. Jurkevich, "ISAR image frame selection and multtemporal integration of feature dynamics," to appear as a NRL Memo. Rep.
[8] G. W. Stimson,Introduction to Airborne Radar. El Segundo, CA: Hughes Aircraft Co., 1983.
[9] C. C. Chen and H. C. Andrews, "Target-motion-induced radar imaging,"IEEE Trans. Aerospace Elect. Syst., vol. ASE-16, pp. 2-14, 1980.
[10] A. L. Gorin, "Effect of data reduction on inverse aperture radar (ISAR) image quality and target shape description,"Opt. Eng., vol. 21, pp. 858-863, 1982.
[11] C. Chu and M. C. K. Yang, "Invariant quantities in regression-induced boundaries under a special linear transformation,"Pattern Recognit., vol. 20, pp. 403-410, 1987.
[12] W. H. Press, B. P. Flannery, S. A. Teukolsky and W. T. Vetterling,Numerical Recipes. Cambridge, MA: Cambridge Univ. Press, 1987.
[13] J. L. Couhat, Ed.,Combat Fleet of the World 1986/87. Annapolis, MD: Naval Inst. Press, 1986.

Index Terms:
image recognition; Bayes methods; statistical analysis; remote sensing; image recognition; multiple images; point matching; ship identification; ISAR image sequence; noise levels; statistical theory
M.C.K. Yang, J.S. Lee, "Object Identification From Multiple Images Based on Point Matching Under a General Transformation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 7, pp. 751-756, July 1994, doi:10.1109/34.297958
Usage of this product signifies your acceptance of the Terms of Use.