Researcher Portfolio
Harmeling, Stefan
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, Max Planck Institute for Biological Cybernetics, Max Planck Society
Researcher Profile
Position: Max Planck Institute for Biological Cybernetics, Max Planck Society
Position: Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society
Position: Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society
Additional IDs: MPIKYB: harmeling
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons83954
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Publications
(1 - 25 of 79)
: Schuler, C. J., Hirsch, M., Harmeling, S., & Schölkopf, B. (2016). Learning to Deblur. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7), 1439-1451. doi:10.1109/TPAMI.2015.2481418. [PubMan] : Schütt, H., Harmeling, S., Macke, J., & Wichmann, F. (2016). Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data. Vision Research, 122, 105-123. doi:10.1016/j.visres.2016.02.002. [PubMan] : Schuler, C. J., Hirsch, M., Harmeling, S., & Schölkopf, B. (2015). Learning to Deblur. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7), 1439-1451. doi:10.1109/TPAMI.2015.2481418. [PubMan] : Schütt, H., Harmeling, S., Macke, J., & Wichmann, F. (2015). Psignifit 4: Pain-free Bayesian Inference for Psychometric Functions. Poster presented at 15th Annual Meeting of the Vision Sciences Society (VSS 2015), St. Pete Beach, FL, USA. [PubMan] : Schölkopf, B., Muandet, K., Fukumizu, K., Harmeling, S., & Peters, J. (2015). Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. Statistics and Computing, 25(4), 755-766. doi:10.1007/s11222-015-9558-5. [PubMan] : Kopp, M., Harmeling, S., Schütz, G., Schölkopf, B., & Fähnle, M. (2015). Towards denoising XMCD movies of fast magnetization dynamics using extended Kalman filter. Ultramicroscopy, 148, 115-122. doi:10.1016/j.ultramic.2014.10.001. [PubMan] : Schütt, H., Harmeling, S., Macke, J., Wichmann, F., & Wichmann, F. A. (2014). Pain-free Bayesian inference for psychometric functions. Poster presented at 2014 European Mathematical Psychology Group Meeting (EMPG), Tübingen, Germany. [PubMan] : Schütt, H., Harmeling, S., Macke, J., & Wichmann, F. (2014). Pain-free bayesian inference for psychometric functions. Poster presented at 37th European Conference on Visual Perception (ECVP 2014), Beograd, Serbia. [PubMan] : Lampert, C., Nickisch, H., & Harmeling, S. (2014). Attribute-based classification for zero-shot visual object categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3), 453-465. doi:10.1109/TPAMI.2013.140. [PubMan] : Zscheischler, J., Reichstein, M., Harmeling, S., Rammig, A., Tomelleri, E., & Mahecha, M. (2014). Extreme events in gross primary production: a characterization across continents. Biogeosciences, 11, 2909-2924. doi:10.5194/bg-11-2909-2014. [PubMan] : Köhler, R., Schuler, C. J., Schölkopf, B., & Harmeling, S. (2014). Mask-Specific Inpainting with Deep Neural Networks. In X. Jiang, J. Hornegger, & R. Koch (Eds. ), Proceedings Pattern Recognition (pp. 523-534). Springer International Publishing. [PubMan] : Zscheischler, J., Mahecha, M., v Buttlar, J., Harmeling, S., Jung, M., Rammig, A., Randerson, J., Schölkopf, B., Seneviratne, S., Tomelleri, E., Zaehle, S., & Reichstein, M. (2014). A few extreme events dominate global interannual variability in gross primary production. Environmental Research Letters, 9(3): 035001. doi:10.1088/1748-9326/9/3/035001. [PubMan] : Burger, H. C., Schuler, C. J., & Harmeling, S. (2013). How to Combine Internal and External Denoising Methods. In J. Weickert, M. Hein, & B. Schiele (Eds. ), Pattern Recognition - Proceedings 35th German Conference (GCPR 2013) (pp. 121-130). Berlin Heidelberg: Springer. [PubMan] : Harmeling, S., Hirsch, M., & Schoelkopf, B. (2013). On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit. In 2013 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2013) (pp. 1083-1090). Piscataway, NJ: IEEE. doi:10.1109/CVPR.2013.144. [PubMan] : Schuler, C. J., Burger, H., Harmeling, S., & Schölkopf, B. (2013). A Machine Learning Approach for Non-blind Image Deconvolution. In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013) (pp. 1067-1074). Piscataway, NJ: IEEE. doi:10.1109/CVPR.2013.142. [PubMan] : Grosse-Wentrup, M., Harmeling, S., Zander, T., Hill, N., & Schölkopf, B. (2013). How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data. In Proceedings of the 3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI 2013) (pp. 102-105). IEEE Computer Society. doi:10.1109/PRNI.2013.35. [PubMan] : Zscheischler, J., Mahecha, M. D., Harmeling, S., & Reichstein, M. (2013). Detection and attribution of large spatiotemporal extreme events in Earth observation data. Ecological Informatics, 15, 66-73. doi:10.1016/j.ecoinf.2013.03.004. [PubMan] : Köhler, R., Hirsch, M., Schölkopf, B., & Harmeling, S. (2013). Improving alpha matting and motion blurred foreground estimation. In Proceedings of the 20th IEEE International Conference on Image Processing (ICIP 2013) (pp. 3446-3450). IEEE Signal Processing Society. Retrieved from http://2013.ieeeicip.org/proc/pdfs/0003446.pdf. [PubMan] : Köhler, R., Hirsch, M., Mohler, B., Schölkopf, B., & Harmeling, S. (2012). Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database. In A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, & C. Schmid (Eds. ), Computer Vision - ECCV 2012 (pp. 27-40). Berlin, Germany: Springer. [PubMan] : Burger, H., Schuler, C., & Harmeling, S. (2012). Image denoising: Can plain Neural Networks compete with BM3D? In 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012) (pp. 2392-2399). Piscataway, NJ, USA: IEEE. [PubMan] : Zscheischler, J., Mahecha, M., & Harmeling, S. (2012). Climate classifications: the value of unsupervised clustering. In International Conference on Computational Science (ICCS 2012 (pp. 897-906). Amsterdam, Netherlands: Elsevier. [PubMan] : Zscheischler, J., Mahecha, M., & Harmeling, S. (2012). Climate classifications: the value of unsupervised clustering. In H. Ali, Y. Shi, D. Khazanchi, M. Lees, G. van Albada, P. Sloot, & J. Dongarra (Eds. ), Procedia Computer Science (pp. 897-906). Amsterdam, Netherlands: Elsevier. [PubMan] : Köhler, R., Hirsch, M., Mohler, B., Schoelkopf, B., & Harmeling, S. (2012). Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database. In A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, & C. Schmid (Eds. ), Computer Vision - ECCV 2012 (pp. 27-40). Berlin, Germany: Springer. [PubMan] : Schuler, C., Hirsch, M., Harmeling, S., & Schölkopf, B. (2012). Blind Correction of Optical Aberrations. In A. Fitzgibbon (Ed. ), Computer Vision - ECCV 2012 (pp. 187-200). Berlin, Germany: Springer. [PubMan] : Burger, H. C., Schuler, C. J., & Harmeling, S. (2012). Image denoising: Can plain Neural Networks compete with BM3D? In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012) (pp. 2392-2399). Piscataway, NJ: IEEE. doi:10.1109/CVPR.2012.6247952. [PubMan]