TY - GEN

T1 - Registration of joint geometric and radiometric image deformations in the presence of noise

AU - Kovalsky, Shahar Z.

AU - Cohen, Guy

AU - Francos, Joseph M.

PY - 2007/12/1

Y1 - 2007/12/1

N2 - We consider the problem of object registration where the observed template simultaneously undergoes an affine transformation of coordinates and a non-linear mapping of the intensities. More generally, the problem is that of jointly estimating the geometric and radiometric deformations relating two observations on the same object. We show that, in the absence of noise, the original high dimensional non-convex search problem that needs to be solved in order to register the observation to the template is replaced by an equivalent problem, expressed in terms of a sequence of two linear systems of equations. A solution to this sequence provides an exact solution to the registration problem. It is further shown that in the presence of noise, the original stochastic registration problem can be mapped, almost surely, to a new deterministic problem in the form of a classic deconvolution problem. Solution of the deconvolution problem reduces the solution of the original estimation problem to the form derived for the noise-free case.

AB - We consider the problem of object registration where the observed template simultaneously undergoes an affine transformation of coordinates and a non-linear mapping of the intensities. More generally, the problem is that of jointly estimating the geometric and radiometric deformations relating two observations on the same object. We show that, in the absence of noise, the original high dimensional non-convex search problem that needs to be solved in order to register the observation to the template is replaced by an equivalent problem, expressed in terms of a sequence of two linear systems of equations. A solution to this sequence provides an exact solution to the registration problem. It is further shown that in the presence of noise, the original stochastic registration problem can be mapped, almost surely, to a new deterministic problem in the form of a classic deconvolution problem. Solution of the deconvolution problem reduces the solution of the original estimation problem to the form derived for the noise-free case.

KW - Image recognition

KW - Image registration

KW - Multidimensional signal processing

KW - Nonlinear estimation

KW - Parameter estimation

UR - http://www.scopus.com/inward/record.url?scp=47849090715&partnerID=8YFLogxK

U2 - 10.1109/SSP.2007.4301321

DO - 10.1109/SSP.2007.4301321

M3 - Conference contribution

AN - SCOPUS:47849090715

SN - 142441198X

SN - 9781424411986

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

SP - 561

EP - 565

BT - 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings

T2 - 2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007

Y2 - 26 August 2007 through 29 August 2007

ER -