
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
W.E.L. Grimson, D.P. Huttenlocher, "On the Verification of Hypothesized Matches in ModelBased Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 12, pp. 12011213, December, 1991.  
BibTex  x  
@article{ 10.1109/34.106994, author = {W.E.L. Grimson and D.P. Huttenlocher}, title = {On the Verification of Hypothesized Matches in ModelBased Recognition}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {13}, number = {12}, issn = {01628828}, year = {1991}, pages = {12011213}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.106994}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  On the Verification of Hypothesized Matches in ModelBased Recognition IS  12 SN  01628828 SP1201 EP1213 EPD  12011213 A1  W.E.L. Grimson, A1  D.P. Huttenlocher, PY  1991 KW  computer vision; nonparametric statistics; hypothesis verification; modelbased recognition; model features; data features; statistical occupancy; random process; probability; random match; computer vision; nonparametric statistics; probability; random processes VL  13 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Modelbased recognition methods generally use ad hoc techniques to decide whether or not a model of an object matches a given scene. The most common such technique is to set an empirically determined threshold on the fraction of model features that must be matched to data features. Conditions under which to accept a match as correct are rigorously derived. The analysis is based on modeling the recognition process as a statistical occupancy problem. This model makes the assumption that pairings of object and data features can be characterized as a random process with a uniform distribution. The authors present a number of examples illustrating that real image data are well approximated by such a random process. Using a statistical occupancy model, they derive an expression for the probability that a randomly occurring match will account for a given fraction of the features of a particular object. This expression is a function of the number of model features, the number of data features, and bounds on the degree of sensor noise. It provides a means of setting a threshold such that the probability of a random match is very small.
[1] N. Ayache and O. D. Faugeras, "HYPER: A new approach for the recognition and positioning of twodimensional objects,"IEEE Trans. Patt. Anal. Machine Intell., vol. 8, no. 1, pp. 4454, 1986.
[2] P. J. Besl and R. C. Jain, "Threedimensional object recognition,"ACM Comput. Surveys, vol. 17, no. 1, pp. 75145, Mar. 1985.
[3] R.T. Chin and C. R. Dyer, "Modelbased recognition in robot vision,"ACM Comput. Surveys, vol. 18, no. 1, pp. 67108, Mar. 1986.
[4] D. T. Clemens, "The recognition of twodimentional modeled objectsin images," M.Sc. thesis, Mass. Inst. Technol., Dept. Elec. Eng. Comput. Sci., 1986.
[5] G. J. Ettinger, "Large hierarchical object recognition using libraries of parameterized model subparts,"Patt. Recog., pp. 3241, 1988.
[6] O. D. Faugeras and M. Hebert, "The representation, recognition, and locating of 3D objects,"Int. J. Robotics Res., vol. 5, no. 3, Fall 1986, pp. 2752.
[7] W. Feller,An Introduction to Probability Theory and Its Applications. New York: Wiley, 1986.
[8] M. A. Fischler and R. C. Bolles, "Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,"Commun. ACM, vol. 24, no. 6, pp. 381395, 1981.
[9] W. E. L. Grimson, "On the recognition of parameterized 2D objects,"Int. J. Comput. Vision, vol. 2, no. 4, pp. 353372, 1989.
[10] W. E. L. Grimson, "The combinatorics of object recognition in cluttered environments using constrained search,"Artificial Intell., vol. 44, nos. 12, pp. 121165, 1990.
[11] W. E. L. Grimson and D. P. Hultenlocher, "On the sensitivity of the Hough transform for object recognition,"IEEE Trans. Patt. Anal. Machine Intelligence, vol. 12, no. 3, pp. 255274, 1990.
[12] W. E. L. Grimson and D. P. Hultenlocher, "On the verification of hypothesized matches in modelbased recognition," MIT AI Lab Memo 1110, 1989.
[13] W. E. L. Grimson and T. LozanoPerez, "Localizing overlapping parts by searching the interpretation tree,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI9, no. 4, July 1987.
[14] D. P. Huttenlocher and S. Ullman, "Recognizing solid objects by alignment with an image,"J. Comput. Vision, vol. 5, no. 2, pp. 195212, 1990.
[15] K. Ikeuchi, "Generating an interpretation tree from a CAD model for 3D object recognition in binpicking tasks,"Int. J. Comput. Vision, vol. 1, no. 2, pp. 145165, 1987.
[16] A. M. Mood, F. A. Graybill, and D. C. Boes,Introduction to the Theory of Statistics. New York, McGrawHill, 1974.
[17] D. W. Murray and D. B. Cook, "Using the orientation of fragmentary 3D edge segments for polyhedral object recognition,"Int. J. Comput. Vision, vol. 2, no. 2, pp. 153169, 1988.
[18] G. Stockman, "Object recognition and localization via pose clustering,"Comp. Vision Graphics Image Processing, vol. 40, pp. 361387, 1987.
[19] D. Thompson and J. Mundy, "Three dimensional model matching from an unconstrained viewpoint,"Proc. Int. Conf. Robotics Automation, 1987, pp. 208220.