Dieses Bild zeigt Andrés Bruhn

Andrés Bruhn

Herr Prof. Dr.-Ing.

Professor für Intelligente Systeme
Studiendekan
Institut für Visualisierung und Interaktive Systeme (VIS)
Abteilung Computer Vision

Kontakt

+49 711 685 88439
+49 711 685 88340

Website
Visitenkarte (VCF)

Universitätsstraße 38
70569 Stuttgart
Deutschland
Raum: 1.459

Sprechstunde

Friday, 15:45-16:45 (ohne Terminvereinbarung)
Friday, 16:45-17:45 (Studiendekan, mit Terminvereinbarung)

Publikationen:
  1. 2024

    1. Scheurer, E., Schmalfuss, J., Lis, A., & Bruhn, A. (2024). Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow. In Proc. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE/CVF. https://arxiv.org/abs/2310.17403
  2. 2023

    1. Mehl, L., Jahedi, A., Schmalfuss, J., & Bruhn, A. (2023, Januar). M-FUSE: Multi-frame Fusion for Scene Flow Estimation. Proc. Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.48550/arXiv.2207.05704
    2. Schmalfuss, J., Scheurer, E., Zhao, H., Karantzas, N., Bruhn, A., & Labate, D. (2023). Blind image inpainting with sparse directional filter dictionaries for lightweight CNNs. Journal of Mathematical Imaging and Vision (JMIV), 65, 323–339. https://doi.org/10.1007/s10851-022-01119-6
    3. Schmalfuss, J., Mehl, L., & Bruhn, A. (2023, Oktober). Distracting Downpour: Adversarial Weather Attacks for Motion Estimation. Proc. International Conference on Computer Vision (ICCV). https://arxiv.org/abs/2305.06716
    4. Mehl, L., Schmalfuss, J., Jahedi, A., Nalivayko, Y., & Bruhn, A. (2023). Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://spring-benchmark.org/
  3. 2022

    1. Schmalfuss, J., Mehl, L., & Bruhn, A. (2022). Attacking Motion Estimation with Adversarial Snow. Proc. ECCV Workshop on Adversarial Robustness in the Real World (AROW). https://doi.org/10.48550/arXiv.2210.11242
    2. Krake, T., Bruhn, A., Eberhardt, B., & Weiskopf, D. (2022). Efficient and Robust Background Modeling with Dynamic Mode Decomposition. Journal of Mathematical Imaging and Vision (JMIV), 64, 364–378. https://doi.org/10.1007/s10851-022-01068-0
    3. Schmalfuss, J., Scholze, P., & Bruhn, A. (2022). A perturbation-constrained adversarial attack for evaluating the robustness of optical flow. In S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, & T. Hassner (Hrsg.), Proc. European Conference on Computer Vision (ECCV) (Bd. 13682, S. 183--200). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-20047-2_11
    4. Philipp, M., Bacher, N., Sauer, S., Mathis-Ullrich, F., & Bruhn, A. (2022). From Chairs To Brains: Customizing Optical Flow For Surgical Activity Localization. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 1–5. https://doi.org/10.1109/ISBI52829.2022.9761704
    5. Jahedi, A., Mehl, L., Rivinius, M., & Bruhn, A. (2022). Multi-Scale RAFT: combining hierarchical concepts for learning-based optical flow estimation. IEEE International Conference on Image Processing (ICIP). https://doi.org/10.48550/arXiv.2207.12163
  4. 2021

    1. Mehl, L., Beschle, C., Barth, A., & Bruhn, A. (2021). An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation. Proceedings of the International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 140--152. https://doi.org/10.1007/978-3-030-75549-2_12
  5. 2020

    1. Kurzhals, K., Rodrigues, N., Koch, M., Stoll, M., Bruhn, A., Bulling, A., & Weiskopf, D. (2020). Visual Analytics and Annotation of Pervasive Eye Tracking Video. Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), 16:1-16:9. https://doi.org/10.1145/3379155.3391326
    2. Men, H., Hosu, V., Lin, H., Bruhn, A., & Saupe, D. (2020). Subjective annotation for a frame interpolation benchmark using artefact amplification. Quality and User Experience, 5(1), Article 1. https://doi.org/10.1007/s41233-020-00037-y
    3. Men, H., Hosu, V., Lin, H., Bruhn, A., & Saupe, D. (2020). Visual Quality Assessment for Interpolated Slow-Motion Videos Based on a Novel Database. Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), 1–6. https://doi.org/10.1109/QoMEX48832.2020.9123096
  6. 2019

    1. Men, H., Lin, H., Hosu, V., Maurer, D., Bruhn, A., & Saupe, D. (2019). Visual Quality Assessment for Motion Compensated Frame Interpolation. Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), 1–6. https://doi.org/10.1109/QoMEX.2019.8743221
  7. 2018

    1. Maurer, D., & Bruhn, A. (2018). ProFlow: Learning to Predict Optical Flow. Proceedings of the British Machine Vision Conference (BMVC), 86:1-86:13. https://doi.org/arXiv:1806.00800
    2. Maurer, D., Marniok, N., Goldluecke, B., & Bruhn, A. (2018). Structure-from-motion-aware PatchMatch for Adaptive Optical Flow Estimation. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Hrsg.), Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science (Bd. 11212, S. 575–592). Springer International Publishing. https://doi.org/10.1007/978-3-030-01237-3_35
    3. Maurer, D., Ju, Y. C., Breuß, M., & Bruhn, A. (2018). Combining Shape from Shading and Stereo: A Joint Variational Method for Estimating Depth, Illumination and Albedo. International Journal of Computer Vision, 126(12), Article 12. https://doi.org/10.1007/s11263-018-1079-1
    4. Maurer, D., Stoll, M., & Bruhn, A. (2018). Directional Priors for Multi-Frame Optical Flow. Proceedings of the British Machine Vision Conference (BMVC), 106:1-106:13. http://bmvc2018.org/contents/papers/0377.pdf
  8. 2017

    1. Stoll, M., Maurer, D., Volz, S., & Bruhn, A. (2017). Illumination-Aware Large Displacement Optical Flow. In E. Hancock & M. Pelillo (Hrsg.), Proceedings of International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR). Lecture Notes in Computer Science. Springer.
    2. Kurzhals, K., Stoll, M., Bruhn, A., & Weiskopf, D. (2017). FlowBrush: Optical Flow Art. Proceedings of Computational Aesthetics 2017. http://dx.doi.org/10.1145/3092912.3092914
    3. Maurer, D., Bruhn, A., & Stoll, M. (2017). Order-Adaptive and Illumination-Aware Variational Optical Flow Refinement. Proceedings of the British Machine Vision Conference (BMVC).
    4. Maurer, D., Stoll, M., Volz, S., Gairing, P., & Bruhn, A. (2017). A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow. In F. Lauze, Y. Dong, & A. B. Dahl (Hrsg.), Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science (Bd. 10302, S. 537–549). Springer International Publishing. https://doi.org/10.1007/978-3-319-58771-4_43
    5. Maurer, D., Stoll, M., & Bruhn, A. (2017). Order-adaptive Regularisation for Variational Optical Flow: Global, Local and in Between. In F. Lauze, Y. Dong, & A. B. Dahl (Hrsg.), Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science (Bd. 10302, S. 550–562). Springer International Publishing. https://doi.org/10.1007/978-3-319-58771-4_44
    6. Stoll, M., Maurer, D., & Bruhn, A. (2017). Variational Large Displacement Optical Flow Without Feature Matches. In M. Pelillo & E. R. Hancock (Hrsg.), Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2017. Lecture Notes in Computer Science (Bd. 10746, S. 79–92). Springer International Publishing. https://doi.org/10.1007/978-3-319-78199-0_6
    7. Stoll, M., Volz, S., Maurer, D., & Bruhn, A. (2017). A Time-Efficient Optimisation Framework for Parameters of Opitical Flow Methods. In R. Jenssen, P. Sharma, & F. M. Bianchi (Hrsg.), Proceedings of Scandinavian Conference on Image Analysis (SCIA). Lecture Notes in Computer Science (Bd. 10269). Springer.
  9. 2016

    1. Ju, Y.-C., Maurer, D., Breuß, M., & Bruhn, A. (2016). Direct Variational Perspective Shape from Shading with Cartesian Depth Parametrisation. In M. Breuß, P. Maragos, & S. Wuhrer (Hrsg.), Perspectives on Shape From Shading. Mathematics and Visualization. Springer.
    2. Maurer, D., Ju, Y.-C., Breuß, M., & Bruhn, A. (2016). Combining shape from shading and stereo: a variational approach for the joint estimation of depth, illumination and albedo. Proceedings of the British Machine Vision Conference (BMVC).
    3. Weickert, J., Grewenig, S., Schroers, C., & Bruhn, A. (2016). Cyclic Schemes for PDE-based Image Analysis. International Journal of Computer Vision, 118(3), Article 3.
  10. 2015

    1. Ju, Y.-C., Breuß, M., & Bruhn, A. (2015). Variational Perspective Shape from Shading. In J.-F. Aujol, M. Nikolova, & N. Papadakis (Hrsg.), Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2015). Lecture Notes in Computer Science. Springer.
    2. Maurer, D., Ju, Y.-C., Breuß, M., & Bruhn, A. (2015). An Efficient Linearisation Approach for Variational Perspective Shape from Shading. In J. Gall, P. Gehler, & B. Leibe (Hrsg.), German Conference on Pattern Recognition (GCPR 2015). Lecture Notes in Computer Science (Bd. 9358). Springer. http://dx.doi.org/10.1007/978-3-319-24947-6_20
    3. Bruhn, A., Imiya, A., Leonardis, A., & Pajdla, T. (2015). Vision for Autonomous Vehicles and Probes (Dagstuhl Seminar 15461). Dagstuhl Reports 5(11): 36-61.
  11. 2014

    1. Demetz, O., Stoll, M., Volz, S., Weickert, J., & Bruhn, A. (2014). Learning Brightness Transfer Functions for the Joint Recovery of Illumination Changes and Optical Flow. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Hrsg.), Proceedings of European Conference on Computer Vision (ECCV 2014). Lecture Notes in  Computer Science (Bd. 8689). Springer. http://dx.doi.org/10.1007/978-3-319-10590-1_30
    2. Bruhn, A., Pock, T., & Tai, X.-C. (Eds. ). (2014). Efficient Algorithms for Global Optimisation Problems in Computer Vision, LNCS 8293. In A. Kuiijper, T. Pock, K. Bredies, & H. Bischof (Hrsg.), Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2013). Lecture Notes in Computer Science (8293. Aufl.). Springer.
    3. Schmaltz, C., Peter, P., Mainberger, M., Ebel, F., Weickert, J., & Bruhn, A. (2014). Understanding, optimising, and extending data compression with anisotropic diffusion. International Journal of Computer Vision, 108(3), Article 3. http://dx.doi.org/10.1007/s11263-014-0702-z
  12. 2013

    1. Persch, N., Elhayek, A., Welk, M., Bruhn, A., Grewenig, S., Böse, K., Kraegeloh, A., & Weickert, J. (2013). Enhancing 3-D cell structures in confocal and STED microscopy: a joint model for interpolation, deblurring and anistropic smoothing. Measurement Science and Technology, 24(12), Article 12. http://dx.doi.org/10.1088/0957-0233/24/12/125703
    2. Stoll, M., Volz, S., & Bruhn, A. (2013). Joint trilateral filtering for multiframe optical flow. In I. C. S. Press (Hrsg.), Proceedings of IEEE International Conference on Image Processing (ICIP 2013) (Bd. 2013). IEEE Computer Society Press. http://dx.doi.org/10.1109/ICIP.2013.6738792
    3. Stoll, M., Krüger, R., Ertl, T., & Bruhn, A. (2013). Racecar Tracking and its Visualization Using Sparse Data. 1st IEEE VIS Workshop on Sports Data Visualization, 2013.
    4. Ju, Y.-C., Tozza, S., Breuß, M., Bruhn, A., & Kleefeld, A. (2013). Generalised perspective shape from shading with Oren-Nayar reflectance. Proceedings of the British Machine Vision Conference (BMVC). http://dx.doi.org/10.5244/C.27.42
    5. Stoll, M., Volz, S., & Bruhn, A. (2013). Adaptive integration of feature matches into variational optical flow methods. In K. M. Lee, J. Rehg, Y. Matsushita, & Z. Hu (Hrsg.), Proceedings of Asian Conference on Computer Vision (ACCV 2012). Lecture Notes in Computer Science. Springer. http://dx.doi.org/10.1007/978-3-642-37431-9_1
    6. Galliani, S., Ju, Y.-C., Breuß, M., & Bruhn, A. (2013). Generalised perspective shape from shading in spherical coordinates. In A. Kuiijper, T. Pock, K. Bredies, & H. Bischof (Hrsg.), Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2013). Lecture Notes in Computer Science. Springer. http://dx.doi.org/10.1007/978-3-642-38267-3_19
  13. 2012

    1. Valgaerts, L., Bruhn, A., Mainberger, M., & Weickert, J. (2012). Dense versus sparse approaches for estimating the fundamental matrix. International Journal of Computer Vision, 96(2), Article 2. http://dx.doi.org/10.1007/s11263-011-0466-7
    2. Demetz, O., Weickert, J., Bruhn, A., & Zimmer, H. (2012). Optic flow scale space. In A. Bruckstein, B. ter Haar Romeny, A. Bronstein, & M. Bronstein (Hrsg.), Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2011). Lecture Notes in Computer Science (Bd. 6667). Springer. http://dx.doi.org/10.1007/3-540-63931-4_268
    3. Ju, Y.-C., Breuß, M., Bruhn, A., & Galliani, S. (2012). Shape from Shading for Rough Surfaces: Analysis of the Oren-Nayar Model. Proceedings of the British Machine Vision Conference (BMVC). http://dx.doi.org/10.5244/C.26.104
    4. Schroers, C., Zimmer, H., Valgaerts, L., Bruhn, A., Demetz, O., & Weickert, J. (2012). Anisotropic range image integration. Awarded a DAGM-OAGM 2012 Paper Prize. In A. Pinz, T. Pock, H. Bischof, & F. Leberl (Hrsg.), Proceedings of German and Austrian Pattern Recognition Symposium (DAGM-OAGM 2012). Lecture Notes in Computer Science (Bd. 7476). Springer. http://dx.doi.org/10.1007/978-3-642-32717-9_8
    5. Gwosdek, P., Grewenig, S., Bruhn, A., & Weickert, J. (2012). Theoretical foundations of Gaussian convolution by extended box filtering. In A. Bruckstein, B. ter Haar Romeny, A. Bronstein, & M. Bronstein (Hrsg.), Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2011). Lecture Notes in Computer Science (Bd. 6667). Springer. http://dx.doi.org/10.1007/978-3-642-24785-9_38
    6. Lau Raket, L., Roholm, L., Bruhn, A., & Weickert, J. (2012). Motion compensated frame interpolation with a symmetrical optical flow constraint. In B. George, B. Richard, K. Darko, & P. Bharam (Hrsg.), Proceedings of International Symposium on Visual Computing (ISVC 2012). Lecture Notes in Computer Science (Bd. 7431). Springer. http://dx.doi.org/10.1007/978-3-642-33179-4_43
    7. Valgaerts, L., Wu, C., Bruhn, A., Seidel, H.-P., & Theobalt, C. (2012). Lightweight Binocular Facial Performance Capture under Uncontrolled Lighting. ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia), 31(6), Article 6. http://dx.doi.org/10.1145/2366145.2366206
  14. 2011

    1. Bruhn, A., Imiya, A., Leonardis, A., & Pajdla, T. (2011). Efficient Algorithms for Global Optimisation Methods in Computer Vision (Dagstuhl Seminar 11471). Dagstuhl Reports 1(11): 66-90.
    2. Mainberger, M., Bruhn, A., Weickert, J., & Forchhammer, S. (2011). Edge-based compression of cartoon-like images with homogeneous diffusion. Pattern Recognition, 44(9), Article 9.
    3. Volz, S., Bruhn, A., Valgaerts, L., & Zimmer, H. (2011). Modeling temporal coherence for optical flow. In I. C. S. Press (Hrsg.), Proceedings of IEEE International Conference on Computer Vision (ICCV 2011) (Bd. 2011). IEEE Computer Society Press.
    4. Faubel, F., Georges, M., Kumatani, K., Bruhn, A., & Klakow, D. (2011). Improving hands-free speech recognition in a car through audio-visual voice activity detection. Proceedings of Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA 2011).
    5. Zimmer, H., Bruhn, A., & Weickert, J. (2011). Optic flow in harmony. International Journal of Computer Vision, 93(3), Article 3.
    6. Zimmer, H., Bruhn, A., & Weickert, J. (2011). Freehand HDR Imaging of Moving Scenes with Simultaneous Resolution Enhancement. Computer Graphics Forum (Proceedings of EUROGRAPHICS), 30(2), Article 2.
  15. 2010

    1. Gwosdek, P., Zimmer, H., Grewenig, S., Weickert, J., & Bruhn, A. (2010). A highly efficient GPU implementation for variational optic flow based on the Euler-Lagrange framework. In K. Kutulakos (Hrsg.), Proceedings of ECCV Workshop on Computer Vision with GPUs (CVGPU 2010). Springer.
    2. Valgaerts, L., Bruhn, A., Zimmer, H., Weickert, J., Stoll, C., & Theobalt, C. (2010). Joint estimation of motion, structure and geometry from stereo sequences. In K. Danilidis, P. Maragos, & N. Paragios (Hrsg.), Proceedings of European Conference on Computer Vision (ECCV 2010). Lecture Notes in  Computer Science (Bd. 6314). Springer.
    3. Hauger, C., Weigand, H., Weickert, J., & Bruhn, A. (2010). Medizinisch optisches Beobachtungsgerät und Verfahren zum Erstellen einer stereoskopischen Zwischenperspektive in einem derartigen Gerät. Patent DE 10 20008 024 732 B4 2010.04.01, 2010.
    4. Schmaltz, C., Gwosdek, P., Bruhn, A., & Weickert, J. (2010). Electrostatic halftoning. Computer Graphics Forum, 29(8), Article 8.
    5. Gwosdek, P., Weickert, J., & Bruhn, A. (2010). Variational optic flow on the Sony PlayStation 3. Journal of Real-Time Image Processing, 5(3), Article 3.
    6. Grewenig, S., Weickert, J., & Bruhn, A. (2010). From box filtering to fast explicit diffusion. Awarded the DAGM 2010 Main Prize (Best Paper Award). In M. Goesele, S. Roth, A. Kujper, B. Schiele, & K. Schindler (Hrsg.), Proceedings of German Pattern Recognition Symposium (DAGM 2010). Lecture Notes in Computer Science (Bd. 6376). Springer.
  16. 2009

    1. Zimmer, H., Bruhn, A., Weickert, J., Valgaerts, L., Salgado, A., Rosenhahn, B., & Seidel, H.-P. (2009). Complementary optic flow. In D. Cremers, Y. Boykov, A. Blake, & F. R. Schmidt (Hrsg.), Proceedings of International Conference on Energy Minimizing Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009). Lecture Notes in Computer Science (Bd. 5681). Springer.
    2. Schmaltz, C., Weickert, J., & Bruhn, A. (2009). Beating the quality of JPEG 200 with anisotropic diffusion. In J. Denzler, G. Notni, & H. Süße (Hrsg.), Proceedings of German Pattern Recognition Symposium (DAGM 2009). Lecture Notes in Computer Science (Bd. 5748). Springer.
    3. Ghodstinat, M., Bruhn, A., & Weickert, J. (2009). Deinterlacing with motion-compensated anisotropic diffusion. In D. Cremers, B. Rosenhahn, A. Yuille, & F. Schmidt (Hrsg.), Statistical and Geometrical Approaches to Visual Motion Analysis. Lecture Notes in Computer Science (5604. Aufl.). Springer.
  17. 2008

    1. Bruhn, A. (2008). Bewegungsschätzung in Echtzeit mit Optimierungsansätzen. it - Information Technology, 50(1), Article 1.
    2. Valgaerts, L., Bruhn, A., & Weickert, J. (2008). A variational model for the joint recovery of the optical flow and the fundamental matrix. In G. Rigoll (Hrsg.), Proceedings of German Pattern Recognition Symposium (DAGM 2008). Lecture Notes in Computer Science (Bd. 5096). Springer.
    3. Zimmer, H., Bruhn, A., Valgaerts, L., Breuß, M., Weickert, J., Rosenhahn, B., & Seidel, H.-P. (2008). PDE-based anisotropic disparity-driven stereo vision. In O. Deussen, D. Keim, & D. Saupe (Hrsg.), Proceedings of International Workshop on Vision, Modelling, and Visualization (VMV 2008) (Bd. 2008). AKA Heidelberg.
    4. Mainberger, M., Weickert, J., & Bruhn, A. (2008). Is dense optical flow useful to compute the fundamental matrix? Update with errata. In A. Campilho & M. Kamel (Hrsg.), Proceedings of International Conference on Image Analysis and Recognition (ICIAR 2008). Lecture Notes in Computer Science (Bd. 5112). Springer.
    5. Gwosdek, P., Bruhn, A., & Weickert, J. (2008). High performance parallel optical flow algorithms on the Sony Playstation 3. In O. Deussen, D. Keim, & D. Saupe (Hrsg.), Proceedings of International Workshop on Vision, Modeling, and Visualization (VMV 2008) (Bd. 2008). AKA.
    6. Galic, I., Weickert, J., Welk, M., Bruhn, A., Belyaev, A., & Seidel, H.-P. (2008). Image compression with anisotropic diffusion. Journal of Mathematical Imaging and Vision, 31.
  18. 2007

    1. Burgeth, B., Bruhn, A., Didas, S., Weickert, J., & Welk, M. (2007). Morphology for tensor data: Ordering versus PDE-based approach. Image and Vision Computing, 25(4), Article 4.
    2. Vogel, O., Bruhn, A., Weickert, J., & Didas, S. (2007). Direct shape-from-shading with adaptive higher order regularisation. In F. Sgallari, F. Murli, & N. Paragios (Hrsg.), Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2007). Lecture Notes in Computer Science (Bd. 4485). Springer.
    3. Bruhn, A. (2007). Variationsansätze zur Bewegungsschätzung: Präzise Modellierung und effiziente Numerik. In D. W. et al. (Hrsg.), Ausgezeichnete Informatikdissertationen 2006. GI-Edition Lecture Notes in Informatics (LNI). Gesellschaft für Informatik.
    4. Burgeth, B., Papenberg, N., Bruhn, A., Welk, M., & Weickert, J. (2007). Mathematical morphology for matrix fields induced by the Loewner ordering in higher dimensions. Signal Processing, 87(2), Article 2.
    5. Demetz, O., Weickert, J., Welk, M., & Bruhn, A. (2007). Beauty with variational methods: An optic flow approach to hairstyle simulation. In f. Sgallari, A. Murli, & N. Paragios (Hrsg.), Proceedings of International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2007). Lecture Notes in Computer Science (Bd. 4485). Springer.
    6. Mileva, Y., Bruhn, A., & Weickert, J. (2007). Illumination-robust variational optical flow with photometric invariants. In F. A. Hamprecht, C. Schnörr, & B. Jähne (Hrsg.), Proceedings of German Pattern Recognition Symposium (DAGM 2007). Lecture Notes in Computer Science (Bd. 4713). Springer.
    7. Slesareva, N., Bühler, T., Hagenburg, K., Weickert, J., Bruhn, A., Karni, Z., & Seidel, H.-P. (2007). Robust variational reconstruction from multiple views. In B. K. Esboll & K. S. Pedersen (Hrsg.), Proceedings of Scandinavian Conference on Image Analysis (SCIA 2007). Lecture Notes in Computer Science (Bd. 4522). Springer.
  19. 2006

    1. Papenberg, N., Bruhn, A., Brox, T., Didas, S., & Weickert, J. (2006). Highly accurate optic flow computation with theoretically justified warping. International Journal of Computer Vision, 67(2), Article 2.
    2. Weickert, J., Bruhn, A., Brox, T., & Papenberg, N. (2006). A survey on variational optic flow methods for small displacements. In O. Scherzer (Hrsg.), Mathematical models for registration and applications to medical imaging. Springer.
    3. Brox, T., Bruhn, A., & Weickert, J. (2006). Variational motion segmentation with level sets. In H. Bischof, A. Leonardis, & A. Pinz (Hrsg.), Proceedings of European Conference on Computer Vision (ECCV 2006). Lecture Notes in Computer Science (Bd. 3951). Springer.
    4. Mrázek, P., Weickert, J., & Bruhn, A. (2006). On robust estimation and smoothing with spatial and tonal kernels. In R. Klette, R. Kozera, L. Noakes, & J. Weickert (Hrsg.), Geometric Properties from Incomplete Data. Springer.
    5. Bruhn, A. (2006). Variational Optic Flow Computation: Accurate Modelling and Efficient Numerics. Awarded the 2006 Outstanding Dissertation Award by the German Computer Society(GI). Awarded the 2006 Dr. Eduard Martin Prize by the Saarland University. Department of Mathematics and Computer Science, Saarland University.
    6. Bruhn, A., & Weickert, J. (2006). A confidence measure for variational optic flow methods. In R. Klette, R. Kozera, L. Noakes, & J. Weickert (Hrsg.), Geometric Properties from Incomplete Data. Springer.
    7. Bruhn, A., Weickert, J., Kohlberger, T., & Schnörr, C. (2006). A multigrid platform for real-time motion computation with discontinuity-preserving variational methods. International Journal of Computer Vision, 70(3), Article 3.
  20. 2005

    1. Kohlberger, T., Schnörr, C., Bruhn, A., & Weickert, J. (2005). Domain decomposition for variational optical flow computation. IEEE Transactions on Image Processing, 14(8), Article 8.
    2. Bruhn, A., Weickert, J., & Schnörr, C. (2005). Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods. - Update with errata. International Journal of Computer Vision, 61(3), Article 3.
    3. Galic, I., Weickert, J., Welk, M., Bruhn, A., Belyaev, A., & Seidel, H.-P. (2005). Towards PDE-based image compression. In N. Paragios, O. Faugeras, T. Chan, & C. Schnörr (Hrsg.), Proceedings of International Workshop on Variational, Geometric, and Level Set Methods in Computer Vision (VLSM 2005). Lecture Notes in Computer Science (Bd. 3752). Springer.
    4. Kohlberger, T., Schnörr, C., Bruhn, A., & Weickert, J. (2005). Domain decomposition for nonlinear problems: a control-theoretic approach. University of Mannheim.
    5. Burgeth, B., Papenberg, N., Bruhn, A., Welk, M., Feddern, C., & Weickert, J. (2005). Morphology for higher-dimensional tensor data via Loewner ordering. In C. Ronse, L. Najman, & E. Decencière (Hrsg.), Proceedings of International Symposium on Mathematical Morphology (ISMM 2005) (Bd. 30). Springer.
    6. Slesareva, N., Bruhn, A., & Weickert, J. (2005). Optic flow goes stereo: A variational method for estimating discontinuity-preserving dense desparity maps. Awarded a DAGM 2005 Paper Prize. In W. Kropatsch, R. Sablatnig, & A. Hanbury (Hrsg.), Proceedings of German Pattern Recognition Symposium (DAGM 2005). Lecture Notes in Computer Science (Bd. 3663). Springer.
    7. Bruhn, A., Weickert, J., Kohlberger, T., & Schnörr, C. (2005). Discontinuity-preserving computation of variational optic flow in real-time. In R. Kimmel, N. Sochen, & J. Weickert (Hrsg.), Proceedings of International Conference on Scale-Space and PDE Methods in Computer Vision (Scale Space 2005). Lecture Notes in Computer Science (Bd. 3459). Springer.
    8. Bruhn, A., Weickert, J., Feddern, C., Kohlberger, T., & Schnörr, C. (2005). Variational optic flow computation in real-time. IEEE Transactions on Image Processing, 14(5), Article 5.
    9. Bruhn, A., & Weickert, J. (2005). Towards ultimate motion estimation: Combining highest accuracy with real-time performance. Proceedings of IEEE International Conference on Computer Vision (ICCV 2005), 1.
  21. 2004

    1. Bruhn, A., Jakob, T., Fischer, M., Kohlberger, T., Weickert, J., Brüning, U., & Schnörr, C. (2004). High performance cluster computing with 3-D nonlinear diffusion filters. Real-Time Imaging, 10(1), Article 1.
    2. Weickert, J., Bruhn, A., Papenberg, N., & Brox, T. (2004). Variational optic llow computation : From continuous models to algorithms. Proceedings of International Workshop on Computer Vision and Image Analysis (IWCVIA 2003), 26.
    3. Brox, T., Bruhn, A., Papenberg, N., & Weickert, J. (2004). High accuracy optical flow estimation based on a theory for warping. Awarded the ECCV 2004 Longuet-Higgins-Prize (Best Paper Award). In T. Pajdla & J. Matas (Hrsg.), Proceedings of European Conference on Computer Vision (ECCV 2004). Lecture Notes in Computer Science (Bd. 3024). Springer.
    4. Kohlberger, T., Schnörr, C., Bruhn, A., & Weickert, J. (2004). Parallel variational motion estimation by domain decomposition and cluster computing. In T. Pajdla & J. Matas (Hrsg.), Proceedings of European Conference on Computer Vision (ECCV 2004). Lecture Notes in Computer Science (Bd. 3024). Springer.
    5. Papenberg, N., Bruhn, A., Brox, T., & Weickert, J. (2004). Numerical justification for multiresolution optical flow computation. In L. Alvarez (Hrsg.), Proceedings of International Workshop on Computer Vision and Image Analysis (IWCVIA 2003) (Bd. 26).
  22. 2003

    1. Kohlberger, T., Schnörr, C., Weickert, J., & Bruhn, A. (2003). Domain decomposition for parallel veriational optic flow computation. In B. Michaelis & G. Krell (Hrsg.), Proceedings of German Pattern Recognition Symposium (DAGM 2003). Lecture Notes in Computer Science (Bd. 2781). Springer.
    2. Slogsnat, D., Fischer, M., Bruhn, A., Weickert, J., & Brüning, U. (2003). Low level parallelization of nonlinear diffusion filtering algorithms for cluster computing environments. In H. Kosch, L. Böszörményi, & H. Hellwagner (Hrsg.), Proceedings of European Conference on Parallel and Distributed Computing (Euro-Par 2003). Lecture Notes in Computer Science (Bd. 2790). Springer.
    3. Bruhn, A., Weickert, J., Feddern, C., Kohlberger, T., & Schnörr, C. (2003). Real-time optic flow computation with variational methods. In N. Petkov & M. A. Westenberg (Hrsg.), Proceedings of International Conference on Computer Analysis of Images and Patterns (CAIP 2003). Lecture Notes in Computer Science (Bd. 2756). Springer.
 

Computer Vision

  • Motion Estimation
  • 3-D Stereo Reconstruction
  • Image Registration
  • Shape-from-Shading

Scientific Computing

  • Variational Methods
  • Multigrid Methods
  • Machine Learning
  • Efficient Numerics

Image Processing

  • Image Denoising
  • Dithering
  • Image and Video Compression
  • Deinterlacing

Fields of Applications

  • Software/Movie Industry
  • Driver Assistance Systems
  • Pharmaceutical Technology
  • Material Sciences

Lehrangebote von VIS
Lehrangebote am VIS in C@MPUS

In der Lehre trägt die Abteilung zusammen mit den anderen Abteilungen des Instituts zur Ausbildung in den Bachelor- und Master-Programmen der Informatik in Bildverarbeitung, Computer Vision, Mustererkennung und Künstlicher Intelligenz bei.

Andrés Bruhn is a professor of computer science at the University of Stuttgart where he heads the Intelligent Systems group at the Institute for Visualization and Interactive Systems (VIS). He received a diploma degree in Computer Engineering from the University of Mannheim, Mannheim, Germany (2001) and a Ph.D. degree in Computer Science from Saarland University, Saarbrücken, Germany (2006). After working as a postdoctoral researcher (2006-2007) he was associated as an assistant professor with the Mathematical Image Analysis Group at Saarland University, Saarbrücken, Germany (2007-2012). During this time he spent two months as a visiting reasearcher at the University of Las Palmas, Gran Canaria, Spain (2009) and built up an independent research group on Vision and Image Processing within the Cluster of Excellence " Multimodal Computing and Interaction" (2010-2012). His research interests include various areas in the field of computer vision and image processing such as motion estimation, stereo reconstruction, image and video enhancement, data compression as well as the design of efficient numerical schemes for many of the aforementioned tasks. In these fields he published over 100 refereed papers with over 10000 citations and an h-index of 40. Prof. Bruhn serves on the programme committee of numereous conferences including ICCV, ECCV, and CVPR and regularly reviews for all major computer vision journals. His publications received several awards, among them the 2004 Longuet-Higgins Best Paper Award of the European Conference on Computer Vision (ECCV), the 2006 Olympus Prize (German Pattern Recognition Award) of the German Pattern Recognition Society (DAGM), the 2010 Best Paper Award of the German Conference on Pattern Recognition (GCPR) as well as the 2006 GI Outstanding Dissertation Award of the German Society of Computer Science (GI), the Swiss Computer Science Society (SI), the Austrian Computer Society (OCG) and the German Chapters of the ACM (GChACM). Moreover, he was nominated as one of two national finalists for the 2008 Cor Baayen Award of the European Research Consortium for Informatics and Mathematics (ERCIM). Recently, he was awarded the 2014 Koenderink Prize at the European Conference on Computer Vision (ECCV) for fundamental contributions in computer vision.

2022 - ECCV Robust Vision Challenge Award
           Winner of the Category "Optical Flow"
           (with A. Jahedi, M.Luz, M. Rivinius, and L.Mehl)
           awarded by the ECCV Robust Vision Challenge Workshop

2022 - ECCV AROW Best Paper Award
           (with J. Schmalfuss and L.Mehl)
           awarded by the ECCV Workshop on
          Adversarial Robustness in the Real World

2019 -  University of Stuttgart Publication Award
           (with D.Maurer, Y.C.Ju, and M.Breuß)
           awarded by the University of Stuttgart 

2018 - CVPR Robust Vision Challenge Runner-Up Award
           Second Place in the Category "Optical Flow"
           (with D.Maurer)
           awarded by the CVPR Robust Vision Challenge Workshop

2017 - EXPRESSIVE Best Paper Award            
           (with K.Kurzhals, M.Stoll, and D.Weiskopf)

2014 - Koenderink Prize
           for Fundamental Contributions in Computer Vision
           (with T.Brox, N.Papenberg, and J.Weickert)
           awarded by the European Conference on Computer Vision

2014 - ECCV Outstanding Reviewer Award
           awarded by the European Conference on Computer Vision

2012 - DAGM-OAGM Paper Award
           (with C.Schroers, H.Zimmer, L.Valgaerts, O.Demetz and J.Weickert)
           awarded by the German Pattern Recognition Society
           and the Austrian Pattern Recognition Society

2011 - ICCV Outstanding Reviewer Award
           awarded by the International Conference on Computer Vision

2010 - ACCV Outstanding Reviewer Award
           awarded by the Asian Conference on Computer Vision

2010 - DAGM Main Prize (Best Paper Award)
           (with S.Grewenig and J.Weickert)
           awarded by the German Pattern Recognition Society

2008 - ECCV Outstanding Reviewer Award
           awarded by the European Conference on Computer Vision

2008 - ERCIM Cor Baayen Award Finalist
           nominated by the European Research Consortium for Informatics
           and Mathematics

2008 - Excellence in Teaching Award
           awarded by the Computer Science Student Council of Saarland University

2007 - Dr. Eduard Martin Prize
           awarded by the Saarland University

2006 - GI Outstanding Dissertation Award
           awarded by the German Computer Science Society
           the Swiss Computer Science Society
           the Austrian Computer Society
           and the German Chapter of the ACM

2006 - Olympus Prize (German Pattern Recognition Award)
           awarded by the German Pattern Recognition Society

2005 - DAGM Paper Award
           (with N.Slesareva and J.Weickert)
           awarded by the German Pattern Recognition Society

2004 - ECCV Longuet-Higgins Prize (Best Paper Award)
           (with T.Brox, N.Papenberg, and J.Weickert)
           awarded by the European Conference on Computer Vision

2002 - DAGM Paper Award
           (with J.Weickert and C.Schnörr)
           awarded by the German Pattern Recognition Society

 

2017 - German Conference on Pattern Recognition (GCPR, PC)

2017 - International Conference on Scale Space and Variational Methods
          in Computer Vision (SSVM, PC)

2016 - British Machine Vision Conference (BMVC, PC + Session Chair)

2016 - German Conference on Pattern Recognition (GCPR, PC)

2015 - International Conference on Scale Space and Variational Methods
          in Computer Vision (SSVM, PC)

2015 - German Conference on Pattern Recognition (GCPR, PC)

2015 - International Conference on Energy Minimization Methods in Computer Vision
          and Pattern Recognition (EMMCVPR, PC)

2014 - European Conference on Computer Vision (ECCV, PC)
          Awarded an Outstanding Reviewer Award

2014 - German Conference on Pattern Recognition (GCPR, PC)

2014 - IEEE Conference on Computer Vision and Pattern Recognition (CVPR, PC)

2013 - International Workshop on Vision, Modeling and Visualization (VMV, PC)

2013 - German Conference on Pattern Recognition (GCPR, AC + Session Chair)

2013 - International Conference on Energy Minimization Methods in Computer Vision
          and Pattern Recognition (EMMCVPR, PC)

2013 - International Conference on Scale Space and Variational Methods
          in Computer Vision (SSVM, PC)

2012 - Asian Conference on Computer Vision (ACCV, PC + Session Chair)

2012 - German Conference on Pattern Recognition (DAGM, AC + Session Chair)

2012 - IEEE Conference on Computer Vision and Pattern Recognition (CVPR, PC)

2011 - IEEE International Conference on Computer Vision (ICCV, PC)
          Awarded an Outstanding Reviewer Award

2011 - International Conference on Energy Minimization Methods in Computer Vision
          and Pattern Recognition (EMMCVPR, PC)

2011 - IEEE Conference on Computer Vision and Pattern Recognition (CVPR, PC)

2010 - Asian Conference on Computer Vision (ACCV, PC)
          Awarded an Outstanding Reviewer Award

2010 - European Conference on Computer Vision (ECCV, PC)

2010 - International Workshop on Vision, Modeling and Visualization (VMV, PC)

2010 - IEEE Conference on Computer Vision and Pattern Recognition (CVPR, PC)

2010 - International Conference on Computer Vision Theory and Applications (VISAPP, PC)

2009 - International Workshop on Vision, Modeling and Visualization (VMV, PC)

2009 - International Conference on Energy Minimization Methods in Computer Vision
          and Pattern Recognition (EMMCVPR, PC + Session Chair)

2009 - IEEE Conference on Computer Vision and Pattern Recognition (CVPR, PC)

2009 - International Conference on Computer Vision Theory and Applications (VISAPP, PC)

2008 - European Conference on Computer Vision (ECCV, PC)
          Awarded an Outstanding Reviewer Award

2008 - International Workshop on Vision, Modeling and Visualization (VMV, PC)

2008 - Tribute Workshop in Honour of Professor Peter Johansen

2007 - IEEE International Conference on Computer Vision (ICCV, PC)

2007 - International Workshop on Vision, Modeling and Visualization (VMV, PC)

2007 - IEEE Conference on Computer Vision and Pattern Recognition (CVPR, RC)

2007 - Asian Conference on Computer Vision (ACCV, PC)

2006 - IEEE Conference on Computer Vision and Pattern Recognition (CVPR, PC)

2006 - European Conference on Computer Vision (ECCV, PC)

National Science Foundations

  • Chilean Science Foundation (FONDECYT)
  • German Research Foundation (DFG)
  • Israel Science Foundation (ISF)
  • Research Foundation Flanders (FWO)
 
  • IEEE International Conference on Computer Vision (ICCV)
  • European Conference on Computer Vision (ECCV)
  • IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • Asian Conference on Computer Vision (ACCV)
  • International Conference and Exhibition on Computer Graphics and Interactive Techniques (SIGGRAPH)
  • Conference of the European Association for Computer Graphics (EUROGRAPHICS)
  • Conf. and Exhibition on Computer Graphics and Interactive Techniques Asia (SIGGRAPH Asia)
  • Int. Conference on Scale Space and Variational Methods in Computer Vision (SSVM)
  • Int. Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)
  • International Conference on Computer Analysis of Images and Patterns (CAIP)
  • International Conference on Computer Vision Theory and Applications (VISAPP)
  • IEEE Pacific-Rim Symposium on Image and Video Technology (PSIVT)
  • German Conference on Pattern Recognition (GCPR, DAGM)
  • Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)
  • IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision (VLSM)
  • International Workshop on Vision, Modelling and Visualization (VMV)
 
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