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 -