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X. Zhuang, T. Wang, P. Zhang, "A Highly Robust Estimator Through Partially Likelihood Function Modeling and Its Application in Computer Vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 1, pp. 1935, January, 1992.  
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@article{ 10.1109/34.107011, author = {X. Zhuang and T. Wang and P. Zhang}, title = {A Highly Robust Estimator Through Partially Likelihood Function Modeling and Its Application in Computer Vision}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {14}, number = {1}, issn = {01628828}, year = {1992}, pages = {1935}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.107011}, 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  A Highly Robust Estimator Through Partially Likelihood Function Modeling and Its Application in Computer Vision IS  1 SN  01628828 SP19 EP35 EPD  1935 A1  X. Zhuang, A1  T. Wang, A1  P. Zhang, PY  1992 KW  model fitting estimator; pattern recognition; picture processing; partially likelihood function modeling; computer vision; unknown log likelihood function; Bayesian statistical decision rule; single rigid motion estimation; multiple rigid motion segmentation; contaminated Gaussian mixture models; Bayes methods; computer vision; decision theory; estimation theory; pattern recognition; picture processing VL  14 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
The authors present a highly robust estimator, known as the model fitting (MF) estimator for general regression. They explain that high robustness becomes possible through partially but completely modeling the unknown log likelihood function. The partial modeling takes place by taking the Bayesian statistical decision rule and a number of important heuristics into consideration while maximizing the log likelihood function. Applications include the automatic selection of multiple thresholds, single rigid motion estimation or multiple rigid motion segmentation, and estimation from two perspective views. It is believed that the proposed MF estimator will aid in solving many robust estimation problems that demand an estimator that is either highly robust or capable of handling contaminated Gaussian mixture models.
[1] G. Adiv, "Determining threedimensional motion and structure from optical flow generated by several moving objects,"IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI7, no. 4, pp. 384401, July 1985.
[2] J. Aloimonos and C. M. Brown, "Perception of structure from motion," inProc. IEEE Conf. Comput. Vision Patt. Recogn.(Miami Beach, FL), June 2226, 1986, pp. 510517.
[3] C. L. Fennema and W. B. Thompson, "Velocity determination in series containing several moving objects,"Comput. Graphics Image Processing, vol. 9, pp. 301315, 1979.
[4] 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.
[5] S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,"IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI6, no. 6, Nov. 1984.
[6] R. M. Haralick, "Computer vision theory: The lack thereof,"Comput. Vision Graphics Image Processing, vol. 36, pp. 372386, 1986.
[7] R. M. Haralicket al., "Pose estimation from corresponding point data,"IEEE Trans. Syst. Man, Cybern., Aug. 1989.
[8] G. E. Hinton and T. J. Sejnowski, "Optimal perceptual inference," inProc. IEEE Conf. Comput. Vision Patt. Recogn., 1983.
[9] T. S. Huang, "Motion estimation from stereo sequences," inMachine Vision for Inspection and Measurement(H. Freeman, Ed.). New York: Academic, 1989.
[10] P. Huber,Robust Statistics. New York, Wiley, 1981.
[11] C. LonguetHiggins, "A computer algorithm for reconstructing a scene from two projections,"Nature, vol. 293, pp. 133135, 1981.
[12] J. Kittler and J. Illingsworth, "Minimum error thresholding,"Patt. Recogn., vol. 19, no. 1, pp. 4147, 1986.
[13] N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, "Equations of state calculations by fast computing machines,"J. Chem. Phys., vol. 21, pp. 10871091, 1953.
[14] D. W. Murray and B. F. Buxton, "Scene segmentation from visual motion using global optimization,"IEEE Trans. Patt. Analy. Machine Intell., vol. PAMI9, pp. 161180, 1987.
[15] G. Li, "Robust regression," inExploring Data Tables, Trends and Shapes(D.C. Hoaglin, F. Mosteller and J. W. Tukey, Eds. New York: Wiley, 1985, pp. 281343.
[16] J. W. Roach and J. K. Aggarwal, "Determining the movement of objects from a sequence of images,"IEEE Trans. Patt. Analysis Machine Intell., vol. PAMI2, pp. 554562, 1980.
[17] P. J. Rousseeuw and A. M. Leroy,Robust Regression&Outlier Detection. New York: Wiley, 1987.
[18] E. H. Thompson, "A rational algebraic formulation of the problem of relative orientation,"Photogrammetric Record, vol. III, no. 14, pp. 152159, 1959.
[19] R. Y. Tsai and T. S. Huang, "Uniqueness and estimation of 3D motion parameters of rigid objects with curved surfaces,"IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI6, pp. 1317, 1984.
[20] S. Ullman,The Interpretations of Visual Motion. Cambridge, MA: MIT Press, 1979.
[21] X. Zhuang, T. S. Huang, and R. M. Haralick, "Twoview motion analysis: A unified algorithm,"Optical Soc. Amer., vol. 3, no. 9, pp. 14921500, 1986.
[22] X. Zhuang, "A simplification to linear two view motion algorithms,"Comput. Vision Graphics Image Processing, vol. 46, pp. 175178, 1989.
[23] X. Zhuang, T. S. Huang, and R. H. Haralick, "A simple procedure to solve motion and structure from three orthographic views,"IEEE J. Robotics Automat., vol. 4, pp. 236239, Apr. 1988.