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Applications use none none integration toinclude none with noneadd barcode existing pdf .net c# [17.57] G. Stiny none for none and J.

Gips, Algorithmic Aesthetics: Computer Models for Criticism and Design in the Arts, University of California Press, 1972. [17.58] M.

Swain and D. Ballard, Color Indexing, International Journal of Computer Vision, 7(1), 1991. [17.

59] T. Syeda-Mahmood, Indexing of Technical Line Drawing Databases, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(8), 1999. [17.

60] H. Takeda, C. Facchinetti, and J.

Latombe, Planning the Motions of a Mobile Robot in a Sensory Uncertainty Field, IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(10), 1994. [17.61] M.

Tichem and M. Cohen, Submicron Registration of Fudicial Marks using Machine Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(8), 1994. [17.

62] S. Trika and R. Kashyap, Geometric Reasoning for Extraction of Manufacturing Features in Iso-oriented Polyhedrons, IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(11), 1994.

[17.63] L. Tsap, D.

Goldgof, and S. Sarkar, Nonrigid Motion Analysis Based on Dynamic Re nement of Finite Element Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(5), 2000. [17.

64] T. Wakahara, Shape Matching using LAT and its Application to Handwritten Numeral Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 1994. [17.

65] R. Wallace, A. Stentz, C.

Thorpe, H. Moravec, W. Whittaker, and T.

Kanade, First Results in Robot Road-Following, Proceedings of the International Joint Conference on Arti cial Intelligence, 1985. [17.66] C.

Wang, Collision Detection of a Moving Polygon in the Presence of Polygonal Obstacles in the Plane, IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 1994. [17.67] C.

Wang and W. Snyder, MAP Transmission Image Reconstruction via Mean Field Annealing for Segmented Attenuation Correction of PET Imaging, 17th International Conference of the IEEE Engineering in Medicine and Biology Society, Montreal, September, 1995. [17.

68] C. Wang and W. Snyder, Frequency Characteristic Study Of Filtered-Backprojection Reconstruction And Maximum Reconstruction For PET Images, 17th International Conference of the IEEE Engineering in Medicine and Biology Society, Montreal, September, 1995.

[17.69] C. Wang, W.

Snyder, and G. Bilbro, Performance Evaluation of Filtered Backprojection Reconstruction and Iterative Reconstruction Methods for PET Images, Computers in Medicine and Biology, 9(3), 1998. [17.

70] C. Wang, W. Snyder, G.

Bilbro, and P. Santago, A Performance Evaluation of FBP and ML Algorithms for PET Imaging, SPIE Medical Imaging, 1996. [17.

71] D. Weinshall and W. Werman, On View Likelihood and Stability, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2).

[17.72] J. Weng, and S.

Chen, Vision-guided Navigation using SHOSLIF, Neural Networks, 1998.. Birt Reports Issues Bibliography [17.73] M. Wheele none none r and K.

Ikeuchi, Sensor Modeling, Probabilistic Hypothesis Generation, and Robust Localization for Object Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(3), 1995. [17.74] Z.

Zhang, R. Weiss, and A. Hanson, Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(1), 1997.

. Automatic target recognition Luke, you ve switched off your targeting computer. What s wrong George Lucas There are lots of transducers which can provide information about targets. In this book, we only consider imaging sensors..

This is the princ none none ipal application chapter of this book.1 We have selected one application area: Automatic target recognition (ATR), and illustrate how the mathematics and algorithms previously covered are used in this application. The point to be made is that almost all applications similarly bene t from not one, but fusions of most of the techniques previously described.

As in previous chapters, we provide the reader with both an explanation of concepts and pointers to more advanced literature. However, since this chapter emphasizes the application, we do not include a Topics section in this chapter. Automatic target/object recognition (ATR) is the term given to the eld of engineering sciences that deals with the study of systems and techniques designed to identify, to locate, and to characterize speci c physical objects (referred to as targets) [18.

7, 18.9, 18.69], usually in a military environment.

Limited surveys of the eld are available [18.3, 18.8, 18.

21, 18.66, 18.74, 18.

79, 18.89]. In this chapter, the only ATR systems considered are those that make use of images.

Therefore, our use of terminology (e.g., clutter) will be restricted to terms that make sense in an imaging scenario.

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