
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
J.M. Jolion, P. Meer, S. Bataouche, "Robust Clustering with Applications in Computer Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 8, pp. 791802, August, 1991.  
BibTex  x  
@article{ 10.1109/34.85669, author = {J.M. Jolion and P. Meer and S. Bataouche}, title = {Robust Clustering with Applications in Computer Vision}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {13}, number = {8}, issn = {01628828}, year = {1991}, pages = {791802}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.85669}, 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  Robust Clustering with Applications in Computer Vision IS  8 SN  01628828 SP791 EP802 EPD  791802 A1  J.M. Jolion, A1  P. Meer, A1  S. Bataouche, PY  1991 KW  statistical analysis; minimum volume ellipsoid robust estimator; iterative methods; constrained random sampling; computer vision; clustering algorithm; unimodal distribution; KolmogorovSmirnov test; feature space; multithresholding; gray level images; Hough space; range image segmentation; computer vision; estimation theory; iterative methods; statistical analysis VL  13 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A clustering algorithm based on the minimum volume ellipsoid (MVE) robust estimator is proposed. The MVE estimator identifies the least volume region containing h percent of the data points. The clustering algorithm iteratively partitions the space into clusters without prior information about their number. At each iteration, the MVE estimator is applied several times with values of h decreasing from 0.5. A cluster is hypothesized for each ellipsoid. The shapes of these clusters are compared with shapes corresponding to a known unimodal distribution by the KolmogorovSmirnov test. The best fitting cluster is then removed from the space, and a new iteration starts. Constrained random sampling keeps the computation low. The clustering algorithm was successfully applied to several computer vision problems formulated in the feature space paradigm: multithresholding of gray level images, analysis of the Hough space, and range image segmentation.
[1] J. R. Bergen and H. Shvaytser, "A probabilistic algorithm for computing Hough transforms," Tech. Rep. SRI David Sarnoff Res. Cen., Princeton, NJ; submitted toJ. Algorithms, 1989.
[2] P. J. Besl and R. C. Jain, "Segmentation through variableorder surface fitting,"IEEE Trans. Patt. Anal. Machine Intell., vol. 10, pp. 167192, 1988.
[3] P. J. Besl, J. B. Birch, and L. T. Watson, "Robust window operations," inProc. IEEE 2nd Int. Conf. Comp. vision, Dec. 1988, pp. 591600.
[4] R. O. Duda and P. E. Hart,Pattern Classification and Scene Analysis. New York: Wiley, 1973.
[5] 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.
[6] K. Fukunaga,Introduction to Statistical Pattern Recognition. New York: Academic, 1972.
[7] F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel,Robust Statistics: An Approach Based on Influence Functions. New York: Wiley, 1986.
[8] R. M. Haralick and L. Watson, "A facet model for image data,"Comput. Graphics Image Proc., vol. 15, pp. 113129, 1981.
[9] R. Hoffman and A. K. Jain, "Segmentation and classification of range images,"IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI9, pp. 608619, 1987.
[10] P. W. Holland and R. E. Welsch, "Robust regression using iteratively reweighted least squares,"Commun. Stat, vol. A6, pp. 813828, 1977.
[11] P. J. Huber,Robust Statistics. New York: Wiley, 1981.
[12] J. Illingworth and J. Kittler, "A survey of the Hough transform,"Comput. Vision Graphics Image Processing, vol. 44, pp. 87116, 1988.
[13] A. K. Jain and R. C. Dubes,Algorithms for Clustering Data. Englewood Cliffs, NJ: PrenticeHall, 1988.
[14] A. K. Jain and R. Hoffman, "Evidencebased recognition of 3D objects,"IEEE Trans. Patt. Anal. Machine Intell., vol. 10, pp. 783803, 1988.
[15] J. M. Jolion, P. Meer, and A. Rosenfeld, "Generalized minimum volume ellipsoid clustering with applications in computer vision," Comput. Vision Lab., Univ. Maryland, College Park, CARTR485, 1990.
[16] N. Kiryati and A. Bruckstein, "Antialiasing the Hough transform,"Proc. 6th Scandinavian Conf. Image Anal.(Oulu, Finland), June 1922, 1989, pp. 621628.
[17] N. Kiryati, Y. Eldar, and A. Bruckstein, "A probabilistic Hough transform,"Patt. Recog., vol. 24, pp. 303316, 1991.
[18] P. Meer, D. Mintz, A. Montanvert, and A. Rosenfeld, "Consensus vision," inProc. AAAI90 Workshop Qualitative Vision(Boston, MA), July 1990, pp. 111115.
[19] P. Meer, D. Mintz, and A. Rosenfeld, "Least median of squares based robust analysis of image structure," inProc. DARPA Image Understanding Workshop(Pittsburgh, PA), Sept. 1990, pp. 231254.
[20] N. Otsu, "A threshold selection method from graylevel histogram,"IEEE Trans. Syst. Man Cybern., vol. SMC9, pp. 6266, 1979.
[21] W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling,Numerical Recipes. Cambridge, England: Cambridge University Press, 1988.
[22] T. Risse, "Hough transform for line recognition: Complexity of evidence accumulation and cluster detection,"Comput. Vision Graphics Image Proc., vol. 46, pp. 327345, 1989.
[23] J. O'Rourke and K. R. Sloan Jr., "Dynamic quantization: Two adaptive data structures for multidimensional spaces,"IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI6, pp. 266280, 1984.
[24] P. J. Rousseeuw and A. M. Leroy,Robust Regression&Outlier Detection. New York: Wiley, 1987.
[25] P. J. Rousseeuw and B. C. van Zomeren, "Unmasking multivariate outliers and leverage points," with comments by R. D. Cook and D. H. Hawkins; D. Ruppert and D. G. Simpson; P. J. Kempthorne and M. B. Mendel; and rejoinder.J. Amer. Stat. Assoc., vol. 85, pp. 633651, 1990.
[26] P. K. Sahoo, S. Soltani, and A. K. C. Wong, "A survey of thresholding techniques,"Comput. Vision Graphics Image Processing, vol. 41, pp. 233260, 1988.
[27] L. Xu, E. Oja, and P. Kultanen, "A new curve detection method: Randomized Hough transform (RHT),"Patt. Recognition Lett., vol. 11, pp. 331338, 1990.
[28] N. Yokoya and M. D. Levine, "Range image segmentation based on differential geometry: A hybrid approach,"IEEE Trans. Patt. Anal. Machine Intell., vol. 11, pp. 643649, 1989.