A Novel Multiresolution based Optimization Scheme for Robust Affine Parameter Estimation
This paper describes a new method for affine parameter
estimation between image sequences. Usually, the parameter
estimation techniques can be done by least squares in a quadratic
way. However, this technique can be sensitive to the presence
of outliers. Therefore, parameter estimation techniques for various
image processing applications are robust enough to withstand the
influence of outliers. Progressively, some robust estimation functions
demanding non-quadratic and perhaps non-convex potentials adopted
from statistics literature have been used for solving these. Addressing
the optimization of the error function in a factual framework for
finding a global optimal solution, the minimization can begin with
the convex estimator at the coarser level and gradually introduce nonconvexity
i.e., from soft to hard redescending non-convex estimators
when the iteration reaches finer level of multiresolution pyramid.
Comparison has been made to find the performance of the results
of proposed method with the results found individually using two
different estimators.
[1] S. Geman and D. Geman, Stochastic relaxation, Gibbs Distributions and
the Bayesian restoration of Images, IEEE Trans. on Pattern Analysis and
Machine Intelligence, 6, 721-741, 1984.
[2] F. C. Jeng and J. W. Woods, Image estimation by stochastic relaxation
in the compound Gaussian case, In Proceedings IEEE Conf. on Acoust.,
Speech, and Signal Proc., 1988.
[3] S. Kirkpatrick and C. D. Gelatt and M. P. Vecchi, Optimization by
Simulated Annealing, Science, 220(4598), 671-680, 1983.
[4] K. Matsui, M. Sase and Y. Kosugi, Medical Image Mapping Using
Collaborative Genetic Algorithm, In Proceedings of Sixteenth Annual
International Conference of the IEEE Engineering in Medicine and
Biology Society, Baltimore, Maryland, U.S.A., 1(2), 612-613, 1994.
[5] B.C.S. Tom, S.N. Efstratiadis and A.K. Katsaggelos, Motion Estimation
of Skeletonized Angiographic Images Using Elastic Registration, IEEE
Transactions on Medical Imaging, 13(3), 450-460, 1994.
[6] V.R. Mandava, J.M. Fitzpatrick and D.R. Pickens, Adaptive Search Space
Scaling in Digital Image Registration, IEEE Transactions on Medical
Imaging, 8(3), 251-262, 1989.
[7] S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multiframe
Super-resolution, IEEE Transactions on Image Processing, 13(10),
1327-1344, 2004.
[8] Ming Ye, Linda G. Shapiro, Robert M. Haralick, Estimating Piecewise-
Smooth Optical Flow with Global Matching and Graduated Optimization,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12),
1625-1630, 2003.
[9] C. Koch, J. L. Marroquin, and A. L. Yuille, Analog neuronal networks
in early vision,In Proceedings of the National Academy of Sciences, pp.
4263-4267, 1986.
[10] D. Geiger and F. Girosi, Parallel and deterministic algorithms for MRFs:
surface reconstruction and integration, Technical Report A. I. Memo, No.
1114, Artificial Intelligence Lab, M. I. T, 1989.
[11] D. Terzopoulos, The Computation of Visible-Surface Representations,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 10,
417-437, 1988.
[12] M. Lefebure and L. Cohen, A multiresolution algorithm for signal and
image registration, in Proc. IEEE Int. Conf. on Image Processing, 3, 252-
255, 1997.
[13] A.Blake, A. Zisserman, Visual Reconstruction, MIT Press, Cambridge,
MA, 1987.
[14] P. J. Green, Bayesian reconstructions from emission tomography data
using a modified EM algorithm, IEEE Transactions on Medical Imaging,
9, 84-93, 1990.
[15] R. R. Schultz and R. L. Stevenson, Stochastic modeling and estimation
of multispectral image data, IEEE Transactions on Image Processing, 4,
1109-1119, 1995.
[16] C. Bouman and K. Sauer, A generalized Gaussian image model for edgepreserving
MAP estimation, IEEE Transactions on Image Processing, 2,
296-310, 1993.
[17] K. Lange, Convergence of EM image reconstruction algorithms with
Gibbs smoothing, IEEE Transactions on Medical Imaging, 9, 439-446,
1990.
[18] Pierre Charbonnier, Laure Blanc-Fraud, Gilles Aubert, Michel Barlaud,
Deterministic edge-preserving regularization in computed imaging, IEEE
Transactions on Image Processing, 6(2), 298-311, 1997.
[19] R. Dahyot, P. Charbonnier, and F. Heitz, Robust visual recognition of
colour images, In proceedings of CVPR, 1, 685-690, 2000.
[20] T. Hebert and R. Leahy, A generalized EM algorithm for 3-D Bayesian
reconstruction from Poisson data using Gibbs priors, IEEE Transactions
on Medical Imaging, 8, 194-202, 1990.
[21] Roger M. Dufour, Eric L. Miller, Nikolas P. Galatsanos, Template
matching based object recognition with unknown geometric parameters.
IEEE Transactions on Image Processing, 11(12), 1385-1396, 2002.
[22] Michael J. Black, P. Anandan, The Robust Estimation of Multiple
Motions: Parametric and Piecewise-Smooth Flow Fields, Computer Vision
and Image Understanding, 63(1), 75-104, 1996.
[23] P. W. Holland and R. E. Welsch, Robust regression using iteratively
reweighed least-squares, Communications in Statistics: Theory and Methods,
A6, 813-827, 1977.
[24] H. Farid and E.P. Simoncelli. Optimally rotation-equivariant directional
derivative kernels. In Proceedings of International Conference on Computer
Analysis of Images and Patterns, pp. 207-214, Berlin, Germany,
1997.
[25] Keith A. Johnson, J. Alex Becker, The whole brain Atlas,
http://www.med.harvard.edu/AANLIB/
[1] S. Geman and D. Geman, Stochastic relaxation, Gibbs Distributions and
the Bayesian restoration of Images, IEEE Trans. on Pattern Analysis and
Machine Intelligence, 6, 721-741, 1984.
[2] F. C. Jeng and J. W. Woods, Image estimation by stochastic relaxation
in the compound Gaussian case, In Proceedings IEEE Conf. on Acoust.,
Speech, and Signal Proc., 1988.
[3] S. Kirkpatrick and C. D. Gelatt and M. P. Vecchi, Optimization by
Simulated Annealing, Science, 220(4598), 671-680, 1983.
[4] K. Matsui, M. Sase and Y. Kosugi, Medical Image Mapping Using
Collaborative Genetic Algorithm, In Proceedings of Sixteenth Annual
International Conference of the IEEE Engineering in Medicine and
Biology Society, Baltimore, Maryland, U.S.A., 1(2), 612-613, 1994.
[5] B.C.S. Tom, S.N. Efstratiadis and A.K. Katsaggelos, Motion Estimation
of Skeletonized Angiographic Images Using Elastic Registration, IEEE
Transactions on Medical Imaging, 13(3), 450-460, 1994.
[6] V.R. Mandava, J.M. Fitzpatrick and D.R. Pickens, Adaptive Search Space
Scaling in Digital Image Registration, IEEE Transactions on Medical
Imaging, 8(3), 251-262, 1989.
[7] S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, Fast and Robust Multiframe
Super-resolution, IEEE Transactions on Image Processing, 13(10),
1327-1344, 2004.
[8] Ming Ye, Linda G. Shapiro, Robert M. Haralick, Estimating Piecewise-
Smooth Optical Flow with Global Matching and Graduated Optimization,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12),
1625-1630, 2003.
[9] C. Koch, J. L. Marroquin, and A. L. Yuille, Analog neuronal networks
in early vision,In Proceedings of the National Academy of Sciences, pp.
4263-4267, 1986.
[10] D. Geiger and F. Girosi, Parallel and deterministic algorithms for MRFs:
surface reconstruction and integration, Technical Report A. I. Memo, No.
1114, Artificial Intelligence Lab, M. I. T, 1989.
[11] D. Terzopoulos, The Computation of Visible-Surface Representations,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 10,
417-437, 1988.
[12] M. Lefebure and L. Cohen, A multiresolution algorithm for signal and
image registration, in Proc. IEEE Int. Conf. on Image Processing, 3, 252-
255, 1997.
[13] A.Blake, A. Zisserman, Visual Reconstruction, MIT Press, Cambridge,
MA, 1987.
[14] P. J. Green, Bayesian reconstructions from emission tomography data
using a modified EM algorithm, IEEE Transactions on Medical Imaging,
9, 84-93, 1990.
[15] R. R. Schultz and R. L. Stevenson, Stochastic modeling and estimation
of multispectral image data, IEEE Transactions on Image Processing, 4,
1109-1119, 1995.
[16] C. Bouman and K. Sauer, A generalized Gaussian image model for edgepreserving
MAP estimation, IEEE Transactions on Image Processing, 2,
296-310, 1993.
[17] K. Lange, Convergence of EM image reconstruction algorithms with
Gibbs smoothing, IEEE Transactions on Medical Imaging, 9, 439-446,
1990.
[18] Pierre Charbonnier, Laure Blanc-Fraud, Gilles Aubert, Michel Barlaud,
Deterministic edge-preserving regularization in computed imaging, IEEE
Transactions on Image Processing, 6(2), 298-311, 1997.
[19] R. Dahyot, P. Charbonnier, and F. Heitz, Robust visual recognition of
colour images, In proceedings of CVPR, 1, 685-690, 2000.
[20] T. Hebert and R. Leahy, A generalized EM algorithm for 3-D Bayesian
reconstruction from Poisson data using Gibbs priors, IEEE Transactions
on Medical Imaging, 8, 194-202, 1990.
[21] Roger M. Dufour, Eric L. Miller, Nikolas P. Galatsanos, Template
matching based object recognition with unknown geometric parameters.
IEEE Transactions on Image Processing, 11(12), 1385-1396, 2002.
[22] Michael J. Black, P. Anandan, The Robust Estimation of Multiple
Motions: Parametric and Piecewise-Smooth Flow Fields, Computer Vision
and Image Understanding, 63(1), 75-104, 1996.
[23] P. W. Holland and R. E. Welsch, Robust regression using iteratively
reweighed least-squares, Communications in Statistics: Theory and Methods,
A6, 813-827, 1977.
[24] H. Farid and E.P. Simoncelli. Optimally rotation-equivariant directional
derivative kernels. In Proceedings of International Conference on Computer
Analysis of Images and Patterns, pp. 207-214, Berlin, Germany,
1997.
[25] Keith A. Johnson, J. Alex Becker, The whole brain Atlas,
http://www.med.harvard.edu/AANLIB/
@article{"International Journal of Engineering, Mathematical and Physical Sciences:63291", author = "J.Dinesh Peter", title = "A Novel Multiresolution based Optimization Scheme for Robust Affine Parameter Estimation", abstract = "This paper describes a new method for affine parameter
estimation between image sequences. Usually, the parameter
estimation techniques can be done by least squares in a quadratic
way. However, this technique can be sensitive to the presence
of outliers. Therefore, parameter estimation techniques for various
image processing applications are robust enough to withstand the
influence of outliers. Progressively, some robust estimation functions
demanding non-quadratic and perhaps non-convex potentials adopted
from statistics literature have been used for solving these. Addressing
the optimization of the error function in a factual framework for
finding a global optimal solution, the minimization can begin with
the convex estimator at the coarser level and gradually introduce nonconvexity
i.e., from soft to hard redescending non-convex estimators
when the iteration reaches finer level of multiresolution pyramid.
Comparison has been made to find the performance of the results
of proposed method with the results found individually using two
different estimators.", keywords = "Image Processing, Affine parameter estimation, Outliers,
Robust Statistics, Robust M-estimators", volume = "2", number = "9", pages = "679-6", }
