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CAGD-Based Computer Vision
November 1989 (vol. 11 no. 11)
pp. 1181-1193

The authors explore the connection between CAGD (computer-aided geometric design) and computer vision. A method for the automatic generation of recognition strategies based on the 3-D geometric properties of shape has been devised and implemented. It uses a novel technique to quantify the following properties of features which compose models used in computer vision: robustness, completeness, consistency, cost, and uniqueness. By utilizing this information, the automatic synthesis of a specialized recognition scheme, called a strategy tree, is accomplished. Strategy trees describe, in a systematic and robust manner, the search process used for recognition and localization of particular objects in the given scene. The consist of selected 3-D features which satisfy system constraints and corroborating evidence subtrees which are used in the formation of hypotheses. Verification techniques, used to substantiate or refute these hypotheses are explored. Experiments utilizing 3-D data are presented.

[1] B. G. Baumgart, "Geometric modeling for computer vision," Dep. Comput. Sci., Stanford Univ.. Tech. Rep. AIM-249, STAN-CS-74- 463, Oct. 1974.
[2] B. Bhanu and C.-C. Ho, "CAD-based 3D object representation for robot vision,"Computer, vol. 20, pp. 19-35, Aug. 1987.
[3] R. C. Bolles and R. A. Cain, "Recognizing and locating partially visible objects: The local-feature-focus method,"Robotics Res., vol. 1, no. 3, pp. 57-82, 1982.
[4] R. C. Bolles and P. Horaud, "3DPO: A three dimensional part orientation svstem,"Int. J. Robotics Res., vol. 5, no. 3, Fall 1986, pp. 3-26.
[5] M. Brady, J. Ponce, A. Yuille, and H. Asada, "Describing surfaces," inProc. 2nd Int. Symp. Robotics Research. Cambridge, MA: MIT Press, 1985, pp. 45-151.
[6] C. Goad, "Special purpose, automatic programming for 3D model-based vision," inProc. DARPA Image Understanding Workshop, 1983, pp. 94-104.
[7] C. Hansen and T. C. Henderson, "The UTAH range database," Dep. Comput. Sci., Univ. Utah, Tech. Rep. UUCS-86-113, Apr. 1986.
[8] C. D. Hansen, "CAGD-based computer vision," Ph.D. dissertation, Univ. Utah, Salt Lake City, Aug. 1988.
[9] C. C. Ho, "CAGD-based 3-D object representations for computer vision," Master's thesis, Univ. Utah, Salt Lake City, Dec. 1987.
[10] K. Ikeuchi, "Model-based interpretation of range imagery," inProc. DARPA Image Understanding Workshop, 1987, pp. 321-339.
[11] E. W. Kent, M. O. Schneir, and T.-H. Hong, "Building representations from fusions of multiple views," inProc. IEEE Conf. Robotics and Automation, San Francisco, CA, Apr. 1986, pp. 1634-1639.
[12] T. F. Knoll and R. C. Jain, "Recognizing partially visible objects using feature indexed hypotheses,"IEEE J. Robotics Automat., vol. 2, pp. 3-13, Mar. 1986.
[13] J. J. Koenderink and A J. Van Doorn, "The singularities of the visual mapping,"Biol. Cybern., vol. 24, pp. 51-59, 1976.
[14] H. Plantinga and C. Dyer, "The aspect representation," Dep. Com put. Sci., Univ. Wisconsin-Madison, Tech. Rep. CSTR-683, Jan. 1987.

Index Terms:
3D shape recognition; computerised pattern recognition; computer vision; CAGD; computer-aided geometric design; robustness; completeness; consistency; strategy tree; search process; CAD; computer vision; computerised pattern recognition; solid modelling; trees (mathematics)
C. Hansen, T.C. Henderson, "CAGD-Based Computer Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 11, pp. 1181-1193, Nov. 1989, doi:10.1109/34.42856
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