@phdthesis {kondermann2009,
	title = {Postprocessing and Restoration of Optical Flows},
	year = {2009},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Mathematik und Informatik, Univ.\ Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/9681},
	author = {Claudia Kondermann}
}
@conference {andres2008,
	title = {On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008},
	year = {2008},
	pages = {1--6},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Linear inverse problems in computer vision, including motion estimation, shape fitting and image reconstruction, give rise to parameter estimation problems with highly correlated errors in variables. Established total least squares methods estimate the most likely corrections Acirc and bcirc to a given data matrix [A, b] perturbed by additive Gaussian noise, such that there exists a solution y with [A + Acirc, b +bcirc]y = 0. In practice, regression imposes a more restrictive constraint namely the existence of a solution x with [A + Acirc]x = [b + bcirc]. In addition, more complicated correlations arise canonically from the use of linear filters. We, therefore, propose a maximum likelihood estimator for regression in the general case of arbitrary positive definite covariance matrices. We show that Acirc, bcirc and x can be found simultaneously by the unconstrained minimization of a multivariate polynomial which can, in principle, be carried out by means of a Grobner basis. Results for plane fitting and optical flow computation indicate the superiority of the proposed method.},
	doi = {10.1109/CVPR.2008.4587571},
	author = {Bj{\"o}rn Andres and Claudia Kondermann and Daniel Kondermann and Fred A. Hamprecht and Christoph S. Garbe}
}
@article {andres_08_errors-in-variables,
	title = {On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation},
	journal = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	note = {1},
	pages = {1-6},
	doi = {10.1109/CVPR.2008.4587571},
	author = {Bj{\"o}rn Andres and Claudia Kondermann and Daniel Kondermann and Ullrich K{\"o}the and Fred A. Hamprecht and Christoph S. Garbe}
}
@incollection {acker2008,
	title = {Inverse Problems and Parameter Identification in Image Processing},
	year = {2008},
	pages = {111--151},
	publisher = {Springer},
	abstract = {Many problems in imaging are actually inverse problems. One reason for this is that conditions and parameters of the physical processes underlying the actual image acquisition are usually not known. Examples for this are the inhomogeneities of the magnetic field in magnetic resonance imaging (MRI) leading to nonlinear deformations of the anatomic structures in the recorded images, material parameters in geological structures as unknown parameters for the simulation of seismic wave propagation with sparse measurement on the surface, or temporal changes in movie sequences given by intensity changes or moving image edges and resulting from deformation, growth and transport processes with unknown fluxes. The underlying physics is mathematically described in terms of variational problems or evolution processes. Hence, solutions of the forward problem are naturally described by partial differential equations. These forward models are reflected by the corresponding inverse problems as well. Beyond these concrete, direct modeling links to continuum mechanics abstract concepts from physical modeling are successfully picked up to solve general perceptual problems in imaging. Examples are visually intuitive methods to blend between images showing multiscale structures at different resolution or methods for the analysis of flow fields.},
	doi = {10.1007/978-3-540-75632-3_4},
	author = {Jens F. Acker and Benjamin Berkels and Kristian Bredies and Mamadou S. Diallo and Marc Droske and Christoph S. Garbe and Matthias Holschneider and Jaroslav Hron and Claudia Kondermann and Michail Kulesh and Peter Maass and Nadine Olischl{\"a}ger and Heinz-Otto Peitgen},
	editor = {R. Dahlhaus and J. Kurths and Timmer, J. and Peter Maass}
}
@conference {kondermann2008b,
	title = {Motion Estimation Based on a Temporal Model of Fluid Flows},
	booktitle = {13th International Symposium on Flow Visualization},
	year = {2008},
	pages = {1-10},
	author = {Daniel Kondermann and Claudia Kondermann and A. Berthe and U. Kertzscher and Christoph S. Garbe}
}
@conference {kondermann2008,
	title = {Postprocessing of optical flows via surface measures and motion inpainting},
	booktitle = {Pattern Recognition},
	volume = {5096},
	year = {2008},
	pages = {355--364},
	abstract = {Dense optical flow fields are required for many applications. They can be obtained by means of various global methods which employ regularization techniques for propagating estimates to regions with insufficient information. However, incorrect flow estimates are propagated as well. We, therefore, propose surface measures for the detection of locations where the full flow can be estimated reliably, that is in the absence of occlusions, intensity changes, severe noise, transparent structures, aperture problems and homogeneous regions. In this way we obtain sparse, but reliable motion fields with lower angular errors. By subsequent application of a basic motion inpainting technique to such sparsified flow fields we obtain dense fields with smaller angular errors than obtained by the original combined local global (CLG) method and the structure tensor method in all test sequences. Experiments show that this postprocessing method makes error improvements of up to 38\% feasible.},
	doi = {10.1007/978-3-540-69321-5_36},
	author = {Claudia Kondermann and Daniel Kondermann and Christoph S. Garbe}
}
@conference {kondermann2008a,
	title = {A statistical confidence measure for optical flows},
	booktitle = {Proceedings of the ECCV},
	volume = {5304},
	year = {2008},
	pages = {290--301},
	abstract = {Confidence measures are crucial to the interpretation of any optical flow measurement. Even though numerous methods for estimating optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated confidence measure for optical flow measurements is still missing. We aim at filling this gap with this contribution, where such a confidence measure is derived, using statistical test theory and measurable statistics of flow fields from the regarded domain. The new confidence measure is computed from merely the results of the optical flow estimator and hence can be applied to any optical flow estimation method, covering the range from local parametric to global variational approaches. Experimental results using state-of-the-art optical flow estimators and various test sequences demonstrate the superiority of the proposed technique compared to existing confidence measures.},
	doi = {10.1007/978-3-540-88690-7_22},
	author = {Claudia Kondermann and Mester, R. and Christoph S. Garbe}
}
@conference {kondermann2007,
	title = {An adaptive confidence measure for optical flows based on linear subspace projections},
	booktitle = {Proceedings of the 29th DAGM Symposium on Pattern Recognition},
	volume = {4713},
	year = {2007},
	pages = {132--141},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Confidence measures are important for the validation of optical flow fields by estimating the correctness of each displacement vector. There are several frequently used confidence measures, which have been found of at best intermediate quality. Hence, we propose a new confidence measure based on linear subspace projections. The results are compared to the best previously proposed confidence measures with respect to an optimal confidence. Using the proposed measure we are able to improve previous results by up to 31\%.},
	doi = {10.1007/978-3-540-74936-3_14},
	author = {Claudia Kondermann and Daniel Kondermann and Bernd J{\"a}hne and Christoph S. Garbe and Christoph Schn{\"o}rr and Bernd J{\"a}hne},
	editor = {Fred A. Hamprecht}
}
