Abstract
This paper presents a methodology for object recognition in complex scenes by learning multiple feature object representations in second generation Forward Looking InfraRed (FLIR) images. A hierarchical recognition framework is developed which solves the recognition task by performing classification using decisions at the lower levels and the input features. The system uses new algorithms for detection and segmentation of objects and a Bayesian formulation for combining multiple object features for improved discrimination. Experimental results on a large database of FLIR images is presented to validate the robustness of the system, and its applicability to FLIR imagery obtained from real scenes.
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References
Rosenfeld, A.: Image analysis: Problems, progress and prospects. PR 17, 3–12 (1984)
Arman, F., Aggarwal, J.K.: Model-based object recognition in dense depth images - a review. ACM Computing Surveys 25(1), 5–43 (1993)
Biederman, I.: Human image understanding: Recent research and a theory. Computer Vision, Graphics and Image Processing 32, 29–73 (1985)
Chu, C., Aggarwal, J.K.: The integration of image segmentation maps using region and edge information. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(12), 1241–1252 (1993)
Manyika, J., Durrant-Whyte, H.F.: Data Fusion and Sensor Management: A decentralized information-theoretic approach. Ellis Horwood (1994)
Nair, D., Aggarwal, J.: Robust automatic target recognition in 2nd generation flir images. In: Proceedings of 3rd IEEE Workshop on Applications of Computer Vision, Sarasota, Florida, December 1996, pp. 311–317 (1996)
Ballard, D., Brown, C.: Computer Vision. Prentice-Hall, Inc., Englewood Cliffs (1982)
Grimson, W.E.L.: Object Recognition by Computer: The role of geometric constraints. MIT Press, Cambridge (1990)
Kittler, J., Hatef, M., Duin, R.P.W.: Combining classifiers. In: International Conference on Pattern Recognition, pp. 897–901 (1996)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39-B, 1–38 (1977)
Shah, S., Aggarwal, J.K.: A Bayesian segmentation framework for textured visual images. In: Proc. of Computer Vision and Pattern Recognition, pp. 1014–1020 (1997)
Shah, S., Aggarwal, J.K.: Multiple feature integration for robust object localization. In: Proc. Computer Vision and Pattern Recognition, Santa Barbara (1998) (to appear)
Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: Proc. of Computer Vision and Pattern Recognition, pp. 84–91 (1994)
Khotanzad, A., Hong, Y.: Invariant image recognition by Zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 489–497 (1990)
Paglieroni, D.: Distance transforms: Properties and machine vision applications. Graphical Models and Image Processing 54, 56–74 (1992)
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© 1999 Springer-Verlag Berlin Heidelberg
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Shah, S., Aggarwal, J.K. (1999). Hierarchical Multifeature Integration for Automatic Object Recognition in Forward Looking Infrared Images. In: Imam, I., Kodratoff, Y., El-Dessouki, A., Ali, M. (eds) Multiple Approaches to Intelligent Systems. IEA/AIE 1999. Lecture Notes in Computer Science(), vol 1611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48765-4_63
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DOI: https://doi.org/10.1007/978-3-540-48765-4_63
Publisher Name: Springer, Berlin, Heidelberg
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