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P

C. Rother, Carlsson, S., and Tell, D., “Projective factorization of planes and cameras in multiple views”, in Proceedings - International Conference on Pattern Recognition, 2002, vol. 16, pp. 737–740.

L. Distributions, “Proof of Lemma 2 Proof of Lemma 3 Proof of Theorem 4 Proof of Lemma 10”, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, pp. 9–11, 2014.

M. Schiegg, Heuer, B., Haubold, C., Wolf, S., Köthe, U., and Hamprecht, F. A., “Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models”, in ISBI. Proceedings, 2015, pp. 394-398.

Technical Report (648.55 KB)

A. Bailoni, Pape, C., Wolf, S., Kreshuk, A., and Hamprecht, F. A., “Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks”, GCPR, vol. 12544. Springer, pp. 331-344, 2020.

D. Heck, “Proximity Graphs for Nonlinear Dimension Reduction”, University of Heidelberg, 2004.

P. Pletscher, Nowozin, S., Kohli, P., and Rother, C., “Putting MAP back on the map”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6835 LNCS, pp. 111–121.

P. Pletscher, Nowozin, S., Kohli, P., and Rother, C., “Putting MAP back on the map”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6835 LNCS, pp. 111–121.

Q

S. Sieg, Stutz, B., Schmidt, T., Hamprecht, F. A., and Maier, W. F., “A QCAR-approach to materials modelling”, Journal of Molecular Modeling, vol. 12, pp. 611-619, 2006.

Technical Report (343.11 KB)

A. H. M. Blom, Brassel, J. - O., von Brocke, M., and Mittler, M., “Quality classification and process control of micro-spot laser welding”, in Proceedings of the Ninth International FAIM Conference - Flexible Automation and Intelligent Manufacturing, Tilburg, 1999, p. 929--941.

X. Lou, Fiaschi, L., Köthe, U., and Hamprecht, F. A., “Quality Classification of Microscopic Imagery with Weakly Supervised Learning”, MICCAI-MLMI. Proceedings, pp. 176-183, 2012.

Technical Report (4.15 MB)

T. König, “Quality Control in Mass Spectrometry”, University of Heidelberg, 2007.

S. Kassemeyer, “Quantification of Tumour Angiogenesis Using Pattern Recognition”, University of Heidelberg, 2009.

C. Leue, “Quantitative Analyse von NOx - Emissionen aus GOME Satellitenbildfolgen”. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1999.

C. Leue, Wenig, M., Wagner, T., Klimm, O., Platt, U., and Jähne, B., “Quantitative analysis of NO$_x$ emissions from Global Ozone Monitoring Experiment satellite image sequences”, J. Geophys. Res., vol. 106, p. 5493--5505, 2001.

D. Schmund, Stitt, M., Jähne, B., and Schurr, U., “Quantitative analysis of the local rates of growth of dicot leaves at a high temporal and spatial resolution, using image sequence analysis”, Plant Journal, vol. 16, p. 505--514, 1998.

B. Andres, Köthe, U., Bonea, A., Nadler, B., and Hamprecht, F. A., “Quantitative Assessment of Image Segmentation Quality by Random Walk Relaxation Times”, in Pattern Recognition. 31st DAGM Symposium, Jena, Germany, September 9-11, 2009. Proceedings, 2009, vol. 5748, pp. 502-511.

Technical Report (3.08 MB)

D. Wierzimok and Hering, F., “Quantitative imaging of transport in fluids with digital particle tracking velocimetry”, in Imaging in Transport Processes, 1993, p. 297--308.

C. Leue, Wenig, M., Jähne, B., and Platt, U., “Quantitative observation of biomass-burning plumes from GOME”, ESA Publications EOQ, vol. 58, p. 33--35, 1998.

J. L. Barron and Spies, H., “Quantitative regularized range flow”, in Vision Interface, 2000, p. 203--210.

R

H. Haußecker, Jähne, B., Geißler, P., and Haußecker, H., “Radiation”, Handbook of Computer Vision and Applications, vol. 1: Sensors and Imaging. Academic Press, p. 7--35, 1999.

H. Haußecker and Jähne, B., “Radiation and illumination”, Computer Vision and Applications - A Guide for Students and Practitioners. Academic Press, p. 11--52, 2000.

M. Erz and Jähne, B., “Radiometric and spectrometric calibrations, and distance noise measurement of TOF cameras”, in 3rd Workshop on Dynamic 3-D Imaging, 2009, vol. 5742, p. 28--41.

H. Gröning, “Radiometrische Kalibrierung und Charakterisierung von CCD- uund CMOS-Bildsensoren und Monokulares 3D-Tracking in Echtzeit”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2003.

H. Haußecker, Jähne, B., Geißler, P., and Haußecker, H., “Radiometry of imaging”, Handbook of Computer Vision and Applications, vol. 1: Sensors and Imaging. Academic Press, p. 103--135, 1999.

H. Haußecker and Jähne, B., “Radiometry of imaging”, Computer Vision and Applications - A Guide for Students and Practitioners. Academic Press, p. 85--109, 2000.

D. Massiceti, Krull, A., Brachmann, E., Rother, C., and Torr, P. H. S., “Random Forests versus Neural Networks − What's best for camera location”. 2017.

A. Eigenstetter, Takami, M., and Ommer, B., “Randomized Max-Margin Compositions for Visual Recognition”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, p. 3590--3597.

Technical Report (8.01 MB)

H. Spies, Jähne, B., and Barron, J. L., “Range flow estimation.”, Computer Vision and Image Understanding, vol. 85, p. 209--231, 2002.

M. Schmidt, Jehle, M., and Jähne, B., “Range flow estimation based on photonic mixing device data”, Int. J. Intelligent Systems Technologies and Applications, vol. 5, p. 380--392, 2008.

M. Schmidt, Jehle, M., and Jähne, B., “Range flow estimation based on photonic mixing device data”, in Proc.\ Dyn3D Workshop, Heidelberg, Sept. 11, 2007, 2007.

J. Weickert and Schnörr, C., “Räumlich–zeitliche Berechnung des optischen Flusses mit nichtlinearen flussabhängigen Glattheitstermen”, in Mustererkennung 1999, 1999, pp. 317–324.

R. Strzodka and Garbe, C. S., “Real-time motion estimation and visualization on graphics cards”, in Proceedings IEEE Visualization 2004, 2004, p. 545--552.

A. Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., “Real-Time Optic Flow Computation with Variational Methods”, in Proc. Computer Analysis of Images and Patterns (CAIP'03), 2003, vol. 2756, pp. 222-229.

O. Hosseini Jafari and Yang, M. Ying, “Real-time RGB-D based template matching pedestrian detection”, in Proceedings - IEEE International Conference on Robotics and Automation, 2016, vol. 2016-June, pp. 5520–5527.

K. Wiehler, Grigat, R. –R., Heers, J., Schnörr, C., and Stiehl, H. –S., “Real–Time Adaptive Smoothing with a 1D Nonlinear Relaxation Network in Analogue VLSI Technology”, in Mustererkennung 1998, Heidelberg, 1998.

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