@conference {7012,
	title = {Network Fusion for Content Creation with Conditional INNs},
	booktitle = {CVPRW 2020 (AI for Content Creation)},
	year = {2020},
	month = {2020},
	abstract = {Artificial Intelligence for Content Creation has the potential to reduce the amount of manual content creation work significantly. While automation of laborious work is welcome, it is only useful if it allows users to control aspects of the creative process when desired. Furthermore, widespread adoption of semi-automatic content creation depends on low barriers regarding the expertise, computational budget and time required to obtain results and experiment with new techniques. With state-of-the-art approaches relying on task-specific models, multi-GPU setups and weeks of training time, we must find ways to reuse and recombine them to meet these requirements. Instead of designing and training methods for controllable content creation from scratch, we thus present a method to repurpose powerful, existing models for new tasks, even though they have never been designed for them. We formulate this problem as a translation between expert models, which includes common content creation scenarios, such as text-to-image and image-to-image translation, as a special case. As this translation is ambiguous, we learn a generative model of hidden representations of one expert conditioned on hidden representations of the other expert. Working on the level of hidden representations makes optimal use of the computational effort that went into the training of the expert model to produce these efficient, low-dimensional representations. Experiments demonstrate that our approach can translate from BERT, a state-of-the-art expert for text, to BigGAN, a state-of-the-art expert for images, to enable text-to-image generation, which neither of the experts can perform on its own. Additional experiments show the wide applicability of our approach across different conditional image synthesis tasks and improvements over existing methods for image modifications.},
	url = {https://compvis.github.io/network-fusion/},
	author = {Robin Rombach and Patrick Esser and Bj{\"o}rn Ommer}
}
@proceedings {7011,
	title = {Network-to-Network Translation with Conditional Invertible Neural Networks},
	year = {2020},
	abstract = {Combining stimuli from diverse modalities into a coherent perception is a striking feat of intelligence of evolved brains. This work seeks its analogy in deep learning models and aims to establish relations between existing networks by faithfully combining the representations of these different domains. Therefore, we seek a model that can relate between different existing representations by learning a conditionally invertible mapping between them. The network demonstrates this capability by (i) providing generic transfer between diverse domains, (ii) enabling controlled content synthesis by allowing modification in other domains, and (iii) facilitating diagnosis of existing representations by translating them into an easily accessible domain. Our domain transfer network can translate between fixed representations without having to learn or finetune them. This allows users to utilize various existing domain-specific expert models from the literature that had been trained with extensive computational resources. Experiments on diverse conditional image synthesis tasks, competitive image modification results and experiments on image-to-image and text-to-image generation demonstrate the generic applicability of our approach. In particular, we translate between BERT and BigGAN, state-of-the-art text and image models to provide text-to-image generation, which neither of both experts can perform on their own.},
	url = {https://compvis.github.io/net2net/},
	author = {Robin Rombach and Patrick Esser and Bj{\"o}rn Ommer}
}
@conference {7025,
	title = {A Note on Data Biases in Generative Models},
	booktitle = {NeurIPS 2020 Workshop on Machine Learning for Creativity and Design},
	year = {2020},
	abstract = {It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes.},
	url = {https://arxiv.org/abs/2012.02516},
	author = {Patrick Esser and Robin Rombach and Bj{\"o}rn Ommer}
}
@conference {Brachmann2019a,
	title = {Neural-guided RANSAC: Learning where to sample model hypotheses},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2019-Octob},
	year = {2019},
	month = {may},
	pages = {4321{\textendash}4330},
	abstract = {We present Neural-Guided RANSAC (NG-RANSAC), an extension to the classic RANSAC algorithm from robust optimization. NG-RANSAC uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets. Previous works use heuristic side-information like hand-crafted descriptor distance to guide hypothesis search. In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks. We present two further extensions to NG-RANSAC. Firstly, using the inlier count itself as training signal allows us to train neural guidance in a self-supervised fashion. Secondly, we combine neural guidance with differentiable RANSAC to build neural networks which focus on certain parts of the input data and make the output predictions as good as possible. We evaluate NG-RANSAC on a wide array of computer vision tasks, namely estimation of epipolar geometry, horizon line estimation and camera re-localization. We achieve superior or competitive results compared to state-of-the-art robust estimators, including very recent, learned ones.},
	isbn = {9781728148038},
	issn = {15505499},
	doi = {10.1109/ICCV.2019.00442},
	url = {http://arxiv.org/abs/1905.04132},
	author = {Brachmann, Eric and Carsten Rother}
}
@mastersthesis {6936,
	title = {Novel Deep Learning-based Instance Segmentation Using Mutex Watershed for Microscopy Cell Images},
	year = {2019},
	school = {Heidelberg University},
	author = {Ravindran, A}
}
@phdthesis {6340,
	title = {Novel Machine Learning Approaches for Neurophysiological Data Analysis},
	year = {2019},
	publisher = {Heidelberg University},
	author = {Kirschbaum, E.}
}
@article {6178,
	title = {Neuron Segmentation with High-Level Biological Priors},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {37},
	year = {2017},
	chapter = {829-839},
	doi = {10.1109/TMI.2017.2712360},
	author = {Krasowki, N and Thorsten Beier and G. W. Knott and Ullrich K{\"o}the and Fred A. Hamprecht and Kreshuk, A.}
}
@conference {Savarino2017,
	title = {Numerical Integration of Riemannian Gradient Flows for Image Labeling},
	booktitle = {Proc. SSVM},
	series = {LNCS},
	volume = {10302},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	author = {Savarino, F. and H{\"u}hnerbein, R. and Astr{\"o}m, F. and Recknagel, J. and Schn{\"o}rr, C.}
}
@article {jaehne2016,
	title = {Noise equalisation and quasi loss-less image data compression {\textendash} or how many bits needs an image sensor?},
	journal = {tm {\textendash} Technisches Messen},
	volume = {83},
	year = {2016},
	pages = {16{\textendash}24},
	doi = {10.1515/teme-2015-0093},
	author = {Bernd J{\"a}hne and Martin Schwarzbauer}
}
@article {Zisler2016,
	title = {Non-Binary Discrete Tomography by Continuous Non-Convex Optimization},
	journal = {IEEE Comp. Imaging},
	volume = {2},
	number = {3},
	year = {2016},
	pages = {335-347},
	author = {M. Zisler and Kappes, J.H. and Schn{\"o}rr, Cl. and Petra, S. and Christoph Schn{\"o}rr}
}
@phdthesis {stapf2015,
	title = {Novel learning-based techniques for dense fluid motion measurements},
	year = {2015},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/18116},
	author = {Julian Stapf}
}
@mastersthesis {urban_14_neural,
	title = {Neural Networks: Optimization and Applications},
	year = {2014},
	school = {University of Heidelberg},
	author = {Urban, G.}
}
@conference {Marquez-Neila2014,
	title = {Non-parametric higher-order random fields for image segmentation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8694 LNCS},
	number = {PART 6},
	year = {2014},
	pages = {269{\textendash}284},
	abstract = {Models defined using higher-order potentials are becoming increasingly popular in computer vision. However, the exact representation of a general higher-order potential defined over many variables is computationally unfeasible. This has led prior works to adopt parametric potentials that can be compactly represented. This paper proposes a non-parametric higher-order model for image labeling problems that uses a patch-based representation of its potentials. We use the transformation scheme of [11, 25] to convert the higher-order potentials to a pair-wise form that can be handled using traditional inference algorithms. This representation is able to capture structure, geometrical and topological information of labels from training data and to provide more precise segmentations. Other tasks such as image denoising and reconstruction are also possible. We evaluate our method on denoising and segmentation problems with synthetic and real images. {\textcopyright} 2014 Springer International Publishing.},
	keywords = {biomedical image analysis, higher-order models, image denoising, Image segmentation, random fields, structured prediction},
	isbn = {9783319105987},
	issn = {16113349},
	doi = {10.1007/978-3-319-10599-4_18},
	author = {M{\'a}rquez-Neila, Pablo and Kohli, Pushmeet and Carsten Rother and Baumela, Luis}
}
@article {bell:VR:2013,
	title = {Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History},
	journal = {Visual Resources Journal, Special Issue on Digital Art History},
	volume = {29},
	number = {1-2},
	year = {2013},
	pages = {26--37},
	publisher = {Taylor \& Francis},
	url = {http://www.tandfonline.com/doi/abs/10.1080/01973762.2013.761111},
	author = {Bell, P. and Schlecht, J. and Bj{\"o}rn Ommer}
}
@article {GSC12:vtv,
	title = {The natural vectorial total variation which arises from geometric measure theory},
	journal = {SIAM Journal on Imaging Sciences},
	year = {2012},
	author = {Bastian Goldl{\"u}cke and E. Strekalovskiy and Daniel Cremers}
}
@article {goldluecke_12_natural,
	title = {The Natural Vectorial Total Variation which Arises from Geometric
Measure Theory},
	journal = {SIAM Journal on Imaging Sciences},
	volume = {5},
	year = {2012},
	note = {1},
	pages = {537-563},
	doi = {10.1137/110823766},
	author = {Bastian Goldl{\"u}cke and E. Strekalovskiy and Daniel Cremers}
}
@booklet {jaehne2012c,
	title = {Neuerungen zum EMVA Standard 1288, Der Release 3.1 des etablierten Standards zur Kameracharakterisierung},
	year = {2012},
	author = {Bernd J{\"a}hne}
}
@conference {Jancsary2012b,
	title = {Non-parametric crfs for image labeling},
	booktitle = {NIPS Workshop Modern Nonparametric Methods in Machine Learning},
	number = {2},
	year = {2012},
	pages = {1{\textendash}5},
	abstract = {We introduce a powerful non-parametric image labeling framework, Regression Tree Fields (RTFs), and discuss its application to image restoration. The conditional structure and the parameters of our model are estimated from training data so as to directly optimize for popular performance measures, resulting in excellent predictive performance at low computational cost.},
	url = {http://www.nowozin.net/sebastian/papers/jancsary2012nonparametriccrf.pdf},
	author = {Jancsary, Jeremy and Nowozin, Sebastian and Carsten Rother}
}
@phdthesis {voss2012,
	title = {Novel Single Camera Techniques for 3D3C Lagrangian Trajectory Measurements in Interfacial Flows},
	year = {2012},
	note = {started 01.02.2009},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/13362},
	author = {Bj{\"o}rn Voss}
}
@conference {Singaraju2010,
	title = {New appearance models for natural image matting},
	year = {2010},
	pages = {659{\textendash}666},
	abstract = {Image matting is the task of estimating a fore- and background layer from a single image. To solve this ill posed problem, an accurate modeling of the scene{\textquoteright}s appearance is necessary. Existing methods that provide a closed form solution to this problem, assume that the colors of the foreground and background layers are locally linear. In this paper, we show that such models can be an overfit when the colors of the two layers are locally constant. We derive new closed form expressions in such cases, and show that our models are more compact than existing ones. In particular, the null space of our cost function is a subset of the null space constructed by existing approaches. We discuss the bias towards specific solutions for each formulation. Experiments on synthetic and real data confirm that our compact models estimate alpha mattes more accurately than existing techniques, without the need of additional user interaction.},
	doi = {10.1109/cvpr.2009.5206491},
	author = {Singaraju, Dheeraj and Carsten Rother and Rhemann, Christoph}
}
@conference {voss2010,
	title = {A new approach for 3C3D measurements of aqueous boundary layer flows relative to the wind-wave undulated interface},
	booktitle = {6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010},
	year = {2010},
	note = {poster},
	doi = {10.5281/zenodo.14930},
	author = {Bj{\"o}rn Voss and Alexander Heinlein and Bernd J{\"a}hne and Christoph S. Garbe}
}
@conference {voss2010a,
	title = {Novel strategy for water sided interfacial 3D3Cflow-visualization using a single camera},
	booktitle = {14th International Symposium on Flow Visualization},
	year = {2010},
	pages = {D1-018},
	author = {Bj{\"o}rn Voss and Christoph S. Garbe}
}
@article {trittler_09_near-optimum,
	title = {Near-Optimum Sampling Design and an Efficient Algorithm for Single Tone Frequency Estimation},
	journal = {Digital Signal Processing},
	volume = {19},
	year = {2009},
	note = {1},
	pages = {628-639},
	doi = {10.1016/j.dsp.2008.10.003},
	author = {Trittler, S. and Fred A. Hamprecht}
}
@conference {Singaraju2009,
	title = {New appearance models for natural image matting},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {659{\textendash}666},
	abstract = {Image matting is the task of estimating a fore- and background layer from a single image. To solve this ill posed problem, an accurate modeling of the scene{\textquoteright}s appearance is necessary. Existing methods that provide a closed form solution to this problem, assume that the colors of the foreground and background layers are locally linear. In this paper, we show that such models can be an overfit when the colors of the two layers are locally constant. We derive new closed form expressions in such cases, and show that our models are more compact than existing ones. In particular, the null space of our cost function is a subset of the null space constructed by existing approaches. We discuss the bias towards specific solutions for each formulation. Experiments on synthetic and real data confirm that our compact models estimate alpha mattes more accurately than existing techniques, without the need of additional user interaction. {\textcopyright} 2009 IEEE.},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206491},
	author = {Singaraju, Dheeraj and Carsten Rother and Rhemann, Christoph}
}
@conference {richter2009a,
	title = {New schemes for fast measurements of air-sea gas exchange in the Aeolotron lab},
	booktitle = {Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009},
	year = {2009},
	abstract = {A number of novel experimental techniques to measure the gas transfer rates of volatile tracers in the wind/wave flume Aeolotron were developed. With the aid of the recently remodeled air conditioning system, the temperature, humidity and air flush rate could be controlled precisely in an open (exchange of air) and closed circulation system. The newly developed experimental schemes were optimized to measure gas transfer rates of half a dozen chemical species simultaneously using FTIR and UV spectroscopy together with thermographic measurements and wind wave measurements. These new experimental schemes are fast enough (temporal resolution less than a minute) to perform measurements in non-stationary and transient conditions. Results of the first series of measurements include precise measurements of the Schmidt number exponent at clean water surfaces.},
	author = {Kerstin E. Richter and Bernd J{\"a}hne}
}
@mastersthesis {mbock2009,
	title = {A Novel Algorithm for Motion Estimation with Explicit Consideration of Perturbations},
	year = {2009},
	school = {University of Heidelberg},
	author = {Mbock, K.}
}
@article {renard_08_nitpick,
	title = {NITPICK: Peak Identification for Mass Spectrometry Data},
	journal = {BMC Bioinformatics},
	volume = {9},
	year = {2008},
	pages = {355},
	doi = {10.1186/1471-2105-9-355},
	author = {B. Y. Renard and Kirchner, M. and Steen, H. and Judith A. J. Steen and Fred A. Hamprecht}
}
@incollection {garbe_08_nonlinear,
	title = {Nonlinear Analysis of Multi-Dimensional Signals},
	year = {2008},
	note = {1},
	pages = {231-288},
	publisher = {Springer},
	doi = {10.1007/978-3-540-75632-3_7},
	author = {Christoph S. Garbe and Kai Krajsek and Pavel Pavlov and Bj{\"o}rn Andres and Matthias M{\"u}hlich and Ingo Stuke and Cicero Mota and Martin B{\"o}hme and Martin Haker and Schuchert, T. and Hanno Scharr and Til Aach and Erhardt Barth},
	editor = {R. Dahlhaus and J. Kurths and Timmer, J. and Peter Maass}
}
@incollection {garbe2008,
	title = {Nonlinear analysis of multi-dimensional signals: local adaptive estimation of complex motion and orientation patterns},
	year = {2008},
	pages = {231-288},
	publisher = {Springer},
	abstract = {We consider the general task of accurately detecting and quantifying orientations in n-dimensional signals s. The main emphasis will be placed on the estimation of motion, which can be thought of as orientation in spatiotemporal signals. Associated problems such as the optimization of matched kernels for deriving isotropic and highly accurate gradients from the signals, optimal integration of local models, and local model selection will also be addressed.},
	doi = {10.1007/978-3-540-75632-3_7},
	author = {Christoph S. Garbe and Kai Krajsek and Pavel Pavlov and Bj{\"o}rn Andres and Matthias M{\"u}hlich and Ingo Stuke and Cicero Mota and Martin B{\"o}hme and Martin Haker and Tobias Schucher and Hanno Scharr and Til Aach and Erhardt Barth},
	editor = {R. Dahlhaus and J. Kurths and Timmer, J. and Peter Maass}
}
@article {jehle2008,
	title = {A novel method for three-dimensional three-component analysis of flow close to free water surfaces},
	journal = {Exp. Fluids},
	volume = {44},
	year = {2008},
	pages = {469--480},
	abstract = {Initial effort is made to establish a new technique for the measurement of three-dimensional three-component (3D3C) velocity fields close to free water surfaces. A fluid volume is illuminated by light emitting diodes (LEDs) perpendicularly to the surface. Small spherical particles are added to the fluid, functioning as a tracer. A monochromatic camera pointing to the water surface from above records the image sequences. The distance of the spheres to the surface is coded by means of a supplemented dye, which absorbs the light of the LEDs according to Beer-Lambert{\textquoteright}s law. By applying LEDs with two different wavelengths, it is possible to use particles variable in size. The velocity vectors are obtained by using an extension of the method of optical flow. The vertical velocity component is computed from the temporal brightness change. The setup is validated with a laminar falling film, which serves as a reference flow. Moreover, the method is applied to buoyant convective turbulence as an example for a non stationary, inherently 3D flow.},
	doi = {10.1007/s00348-007-0453-5},
	author = {Markus Jehle and Bernd J{\"a}hne}
}
@conference {sigg:MLSP:2007,
	title = {Nonnegative CCA for Audiovisual Source Separation},
	booktitle = {International Workshop on Machine Learning for Signal Processing},
	year = {2007},
	pages = {253--258},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Sigg, C. and B. Fischer and Bj{\"o}rn Ommer and Roth, V. and J. M. Buhmann}
}
@conference {jehle2006b,
	title = {A novel method for spatio-temporal analysis of flows within the water-side viscous boundary layer},
	booktitle = {12th Intern. Symp. on Flow Visualization, G{\"o}ttingen, 10--14. September 2006},
	year = {2006},
	doi = {10.5281/zenodo.14845},
	author = {Markus Jehle and Bernd J{\"a}hne}
}
@article {Heiler-Schnoerr-04a,
	title = {Natural Image Statistics for Natural Image Segmentation},
	journal = {Int. J. Comp. Vision},
	volume = {63},
	number = {1},
	year = {2005},
	pages = {5{\textendash}19},
	author = {Heiler, M. and Schn{\"o}rr, C.}
}
@article {restle_04_nonparametric,
	title = {Nonparametric Smoothing of Height maps using {\textquoteleft}{\textquoteleft}Confidence{\textquoteright}{\textquoteright} values},
	journal = {Optical Engineering},
	volume = {43},
	number = {0049},
	year = {2004},
	pages = {866-871},
	doi = {10.1117/1.1666622},
	author = {J. Restle and Hissmann, M. and Fred A. Hamprecht}
}
@conference {Heiler-Schnoerr-03a,
	title = {Natural Statistics for Natural Image Segmentation},
	booktitle = {Proc. IEEE Int. Conf. Computer Vision (ICCV 2003)},
	year = {2003},
	month = {Oct. 13-16},
	pages = {1259-1266},
	address = {Nice, France},
	author = {Heiler, M. and Schn{\"o}rr, C.}
}
@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 {Rother2002,
	title = {A new approach to vanishing point detection in architectural environments},
	booktitle = {Image and Vision Computing},
	volume = {20},
	number = {9-10},
	year = {2002},
	pages = {647{\textendash}655},
	abstract = {A man-made environment is characterized by many parallel lines and orthogonal edges. In this article, a new method for detecting the three mutually orthogonal directions of such an environment is presented. Since real-time performance is not necessary for architectural applications, such as building reconstruction, a computationally intensive approach was chosen. However, this enables us to avoid one fundamental error of most other existing techniques. Compared to theirs, our approach is furthermore more rigorous, since all conditions given by three mutually orthogonal directions are identified and utilized. We assume a partly calibrated camera with unknown focal length and unknown principal point. By examining these camera parameters, which can be determined from orthogonal directions, falsely detected vanishing points may be rejected. {\textcopyright} 2002 Elsevier Science B.V. All rights reserved.},
	keywords = {Architecture, Camera calibration, Geometric constraints, Vanishing lines, Vanishing points},
	issn = {02628856},
	doi = {10.1016/S0262-8856(02)00054-9},
	author = {Carsten Rother}
}
@conference {Cremers-et-al-02a,
	title = {Nonlinear Shape Statistics in Mumford-Shah Based Segmentation},
	booktitle = {Computer Vision -- ECCV 2002)},
	volume = {2351},
	year = {2002},
	pages = {93--108},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {Daniel Cremers and Kohlberger, Timo and Christoph Schn{\"o}rr},
	editor = {Heyden, A. and Sparr, G. and Johansen, P. and Nielsen, M.}
}
@conference {Cremers-et-al-02a,
	title = {Nonlinear Shape Statistics in Mumford-Shah Based Segmentation},
	booktitle = {Computer Vision {\textendash} ECCV 2002)},
	series = {lncs},
	volume = {2351},
	year = {2002},
	pages = {93{\textendash}108},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {Daniel Cremers and Kohlberger, Timo and Christoph Schn{\"o}rr},
	editor = {Heyden, A. and Sparr, G. and Nielsen, M. and Johansen, P.}
}
@conference {schimpf2002,
	title = {Novel insights into heat transfer across the aqueous boundary layer by infrared imagery and its application to air-sea exchange processes},
	booktitle = {Proceedings of Eurotherm 71 on Visualization, Imaging and Data Analysis In Convective Heat and Mass Transfer},
	year = {2002},
	author = {Uwe Schimpf and Christoph S. Garbe and Bernd J{\"a}hne}
}
@conference {Cremers-et-al-01b,
	title = {Nonlinear Shape Statistics via Kernel Spaces},
	booktitle = {Mustererkennung 2001},
	volume = {2191},
	year = {2001},
	pages = {269--276},
	publisher = {Springer},
	organization = {Springer},
	author = {Daniel Cremers and Kohlberger, Timo and Christoph Schn{\"o}rr},
	editor = {B. Radig and S. Florczyk}
}
@conference {Cremers-et-al-01b,
	title = {Nonlinear Shape Statistics via Kernel Spaces},
	booktitle = {Mustererkennung 2001},
	series = {Lect. Notes Comp. Science},
	volume = {2191},
	year = {2001},
	month = {Sept. 12{\textendash}14},
	pages = {269{\textendash}276},
	publisher = {Springer},
	organization = {Springer},
	address = {Munich, Germany},
	author = {Daniel Cremers and Kohlberger, Timo and Christoph Schn{\"o}rr},
	editor = {Radig, B. and Florczyk, S.}
}
@mastersthesis {schulzke2001,
	title = {Numerische Simulation von elementaren Kalziumfreisetzungsereignissen und photolytische Ca^2+-Freisetzung aus K{\"a}figmolek{\"u}len mittels Picosekundenlaser},
	year = {2001},
	school = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Schulzke, Erich}
}
@incollection {jaehne2000d,
	title = {Neighborhood operators},
	year = {2000},
	pages = {273--345},
	publisher = {Academic Press},
	chapter = {9},
	author = {Bernd J{\"a}hne and Bernd J{\"a}hne and Horst Hau{\ss}ecker}
}
@incollection {leue2000,
	title = {NOX Emissions Retrieved from Satellite Images},
	year = {2000},
	pages = {654--655},
	publisher = {Academic Press},
	chapter = {A23},
	author = {Carsten Leue and Mark Wenig and Ulrich Platt and Bernd J{\"a}hne and Horst Hau{\ss}ecker}
}
@incollection {jaehne1999q,
	title = {Neighborhood operators},
	year = {1999},
	pages = {93--124},
	publisher = {Academic Press},
	chapter = {5},
	author = {Bernd J{\"a}hne and Bernd J{\"a}hne and Horst Hau{\ss}ecker},
	editor = {Peter Gei{\ss}ler}
}
@conference {Peckar-et-al-98a,
	title = {Non-Rigid Image Registration Using a Parameter-Free Elastic Model},
	booktitle = {9th British Machine Vision Conference (BMVC{\textquoteleft}98)},
	year = {1998},
	month = {Sept. 14{\textendash}17},
	pages = {134{\textendash}143},
	address = {Southampton/UK},
	author = {Peckar, W. and Schn{\"o}rr, C. and Rohr, K. and Stiehl, H.S.},
	editor = {Carter, J.N. and Nixon, M.S.}
}
@conference {scharr1997,
	title = {Numerische Isotropieoptimierung von FIR-Filtern mittels Quergl{\"a}ttung},
	booktitle = {Proceedings of the 19th DAGM Symposium on Pattern Recognition, Braunschweig},
	year = {1997},
	pages = {199--208},
	doi = {10.1007/978-3-642-60893-3_39},
	author = {Hanno Scharr and K{\"o}rkel, S. and Bernd J{\"a}hne},
	editor = {E. Paulus and F. M. Wahl}
}
@conference {hering1996a,
	title = {A novel system for the combined measurement of wave- and flow-fields beneath wind induced water waves},
	booktitle = {Proc. 18th Int. Congr. for Photogrammetry and Remote Sensing},
	volume = {31},
	number = {B5},
	year = {1996},
	pages = {231--236},
	doi = {10.5281/zenodo.14894},
	url = {http://www.isprs.org/proceedings/XXXI/congress/part5/},
	author = {Frank Hering and G{\"u}nther Balschbach and Bernd J{\"a}hne and P. Waldh{\"a}usl},
	editor = {K. Kraus}
}
@conference {klinke1995b,
	title = {A new instrument for the optical measurement of the fine structure of the water surface in the field},
	booktitle = {IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves},
	number = {19},
	year = {1995},
	pages = {388},
	abstract = {The measurement of the fine structure of the ocean surface constitutes a difficult task. Here, we describe the technical details of a new instrument that allows the full reconstruction of the water surface with sub-millimeter spatial resolution in areas of up to of 15cm x 20cm The single camera system is based on the imaging slope gauge used in previous laboratory experiments to obtain a single slope component of the water surface. The light source was modified such that two arrays of light emitting diodes (LEDs) are used to produce intensity gradients at right anglesJn order to obtain both slope components of the water surface with a monochrome CCD camera, the LED arrays are illuminated successively within a few milliseconds. The whole system is mounted on a the frame of a buoy which follows the orbital motion of the long waves and is critically damped to prevent instability in rougher conditions. Even at high wind speeds, the ambient wave field is not disturbed by the structure. The onboard computer for data acquisition and image processing is remotely controlled from a ship. Battery power allows free-floating operation for a duration of approximately eight hours. In addition to the optical wave slope gauge, the instrument contains a digital gyro sensor which collects stabilized pitch, roll, and magnetic azimuth data, a Young wind speed anemometer, as well as global positioning and temperature sensors.},
	author = {J. Klinke and Bernd J{\"a}hne}
}
@conference {balschbach1995,
	title = {A new instrument to measure steep wind-waves},
	booktitle = {IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves},
	number = {19},
	year = {1995},
	pages = {387},
	abstract = {Current instruments to measure the spatial structure of short waves are limited to quite low slopes. Capillary waves, however, may show slopes well above one (45 degrees inclination). A new instrument has been designed to measure the spatial structure of steep capillary waves. It was designed to specifically address a number of basic questions: Is the limiting form for Crapper capillary waves ever reached? Can a bubble be trapped by such a wave? Do solitary capillary/gravity waves - as recently postulated by Longuet-Higgins - exist in wind/wave fields? The instrument uses the basic design as used in previous instruments of our research group but includes two important enhancements. By the use of a telecentric illumination and camera lens, the influence of wave height on the slope measurement is eliminated. Colored light is used to encode both slope components simultaneously and to correct for uneven intensity distribution in the light source and intensity losses by reflection at the water surface. This contribution describes the setup of the instrument and its calibration in detail. First results from the circular wind/wave facility at Heidelberg University are reported in a separate paper.},
	author = {G{\"u}nther Balschbach and M. Menzel and Bernd J{\"a}hne}
}
@conference {scholz1994,
	title = {A new depth from focus technique for in situ determination of cell concentration in bioreactors},
	booktitle = {Proc. 16. DAGM-Symposium Mustererkennung},
	year = {1994},
	pages = {145--150},
	doi = {10.5281/zenodo.14888},
	author = {Thomas Scholz and Bernd J{\"a}hne and H. Suhr and G. Wehnert and Peter Gei{\ss}ler and K. Schneider}
}
@article {Schnoerr-Sprengel-bcyb-94,
	title = {A Nonlinear Regularization Approach to Early Vision},
	journal = {Biol. Cybernetics},
	volume = {72},
	year = {1994},
	pages = {141{\textendash}149},
	author = {Schn{\"o}rr, C. and Sprengel, R.}
}
@conference {jaehne1993c,
	title = {New trends in image processing hard- and software},
	booktitle = {Proceedings Image Analysis for Pulp and Paper Research and Production},
	year = {1993},
	note = {Invited},
	pages = {1--12},
	author = {Bernd J{\"a}hne}
}
@conference {Sprengel-Schnoerr-dagm-93,
	title = {Nichtlineare Diffusion zur Integration visueller Daten - Anwendung auf Kernspintomogramme},
	booktitle = {Mustererkennung 1993, 15. DAGM-Symposium},
	year = {1993},
	pages = {134{\textendash}141},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {Sprengel, R. and Schn{\"o}rr, C.},
	editor = {P{\"o}ppl, S.J. and Handels, H.}
}
@conference {jaehne1991a,
	title = {New experimental results on the parameters influencing air-sea gas exchange},
	booktitle = {Air-Water Mass Transfer, selected papers from the 2nd International Symposium on Gas Transfer at Water Surfaces, September 11--14, 1990, Minneapolis, Minnesota},
	year = {1991},
	pages = {582--592},
	publisher = {ASCE},
	organization = {ASCE},
	doi = {10.5281/zenodo.12203},
	author = {Bernd J{\"a}hne},
	editor = {Wilhelms, S. C. and John S. Gulliver}
}
@conference {jaehne1987c,
	title = {Neue Ans{\"a}tze zur Bildfolgenanalyse},
	booktitle = {Proc. 9. DAGM-Symposium zur Mustererkennung 1987},
	volume = {149},
	year = {1987},
	pages = {287},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-662-22205-8_70},
	author = {Bernd J{\"a}hne},
	editor = {E. Paulus}
}
@conference {jaehne1984,
	title = {A new optical bubble measuring device; a simple model for bubble contribution to gas exchange},
	booktitle = {Gas transfer at water surfaces},
	year = {1984},
	pages = {237--246},
	publisher = {Reidel},
	organization = {Reidel},
	doi = {10.1007/978-94-017-1660-4_22},
	author = {Bernd J{\"a}hne and Thomas Wais and Michael Barabas},
	editor = {W. Brutsaert and G. H. Jirka}
}
