@incollection {eisele_03_approach,
	title = {A new approach for defect detection in X-ray CT images},
	volume = {2449},
	year = {2003},
	pages = {345-352},
	publisher = {Springer},
	doi = {10.1007/3-540-45783-6},
	author = {H. Eisele and Fred A. Hamprecht},
	editor = {Luc Van Gool}
}
@conference {spies2002b,
	title = {Dense parameter fields from total least squares},
	booktitle = {Proceedings of the 24th DAGM Symposium on Pattern Recognition},
	volume = {LNCS 2449},
	year = {2002},
	pages = {379--386},
	publisher = {Springer},
	organization = {Springer},
	abstract = {A method for the interpolation of parameter fields estimated by total least squares is presented. This is applied to the study of dynamic processes where the motion and further values such as divergence or brightness changes are parameterised in a partial differential equation. For the regularisation we introduce a constraint that restricts the solution only in the subspace determined by the total least squares procedure. The performance is illustrated on both synthetic and real test data.},
	doi = {10.1007/3-540-45783-6_46},
	author = {Hagen Spies and Christoph S. Garbe},
	editor = {Luc Van Gool}
}
@conference {garbe2002e,
	title = {Mixed OLS-TLS for the estimation of dynamic processes with a linear source term},
	booktitle = {Proceedings of the 24th DAGM Symposium on Pattern Recognition},
	volume = {2449},
	year = {2002},
	pages = {463--471},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	abstract = {We present a novel technique to eliminate strong biases in parameter estimation were part of the data matrix is not corrupted by errors. Problems of this type occur in the simultaneous estimation of optical flow and the parameter of linear brightness change as well as in range flow estimation. For attaining highly accurate optical flow estimations under real world situations as required by a number of scientific applications, the standard brightness change constraint equation is violated. Very often the brightness change has to be modelled by a linear source term. In this problem as well as in range flow estimation, part of the data term consists of an exactly known constant. Total least squares (TLS) assumes the error in the data terms to be identically distributed, thus leading to strong biases in the equations at hand. The approach presented in this paper is based on a mixture of ordinary least squares (OLS) and total least squares, thus resolving the bias encountered in TLS alone. Apart from a thorough performance analysis of the novel estimator, a number of applications are presented.},
	doi = {10.1007/3-540-45783-6_56},
	author = {Christoph S. Garbe and Hagen Spies and Bernd J{\"a}hne},
	editor = {Luc Van Gool}
}
