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1993

K. Rohr and Schnörr, C., “An Efficient Approach to the Identification of Characteristic Intensity Variations”, vol. 11, pp. 273–277, 1993.

1997

P. Fornland and Schnörr, C., “Determining the Dominant Plane from Uncalibrated Stereo Vision by a Robust and Convergent Iterative Approach without Correspondence”, in Proc. Int. Conf. Comp. Vision and Patt. Recog. (CVPR'97), San Juan, Puerto Rico, 1997.

2000

D. Cremers, Schnörr, C., Weickert, J., and Schellewald, C., “Diffusion Snakes Using Statistical Shape Knowledge”, in Proc. Algebraic Frames for the Perception-Action Cycle, Kiel, 2000, vol. 1888, pp. 164–174.

D. Cremers, Schnörr, C., Weickert, J., and Schellewald, C., “Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes”, in 3rd Workshop on Dynamic Perception, Berlin, Germany, 2000, vol. 9, pp. 117–122.

2001

D. Cremers, Schnörr, C., and Weickert, J., “Diffusion–Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework”, in IEEE First Workshop on Variational and Level Set Methods in Computer Vision, Vancouver, Canada, 2001, pp. 237–244.

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, 2001, vol. 2191, p. 269--276.

Technical Report (324.55 KB)

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 269–276.

2002

W. Hinterberger, Scherzer, O., Schnörr, C., and Weickert, J., “Analysis of Optical Flow Models in the Framework of Calculus of Variations”, Numer. Funct. Anal. Optimiz., vol. 23, pp. 69–89, 2002.

A. Bruhn, Weickert, J., and Schnörr, C., “Combining the Advantages of Local and Global Optic Flow Methods”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 454–462.

A. Bruhn, Jakob, T., Fischer, M., Kohlberger, T., Weickert, J., Brüning, U., and Schnörr, C., “Designing 3–D Nonlinear Diffusion Filters for High Performance Cluster Computing”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 290–297.

D. Cremers, Tischhäuser, F., Weickert, J., and Schnörr, C., “Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford–Shah functional”, Int. J. Computer Vision, vol. 50, pp. 295–313, 2002.

D. Cremers and Schnörr, C., “Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 472–480.

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision -- ECCV 2002), 2002, vol. 2351, p. 93--108.

Technical Report (636.58 KB)

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision – ECCV 2002), 2002, vol. 2351, pp. 93–108.

2003

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.

D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, p. 1929--1943, 2003.

Technical Report (1.67 MB)

D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, pp. 1929–1943, 2003.

D. Cremers and Schnörr, C., “Statistical Shape Knowledge in Variational Motion Segmentation”, Image and Vision Comp., vol. 21, pp. 77-86, 2003.

D. Cremers, Sochen, N., and Schnörr, C., “Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling”, in Scale Space Methods in Computer Vision, 2003, vol. 2695, pp. 388–400.

D. Cremers, Sochen, N., and Schnörr, C., “Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling”, in Scale Space Methods in Computer Vision, 2003, vol. 2695, p. 388--400.

Technical Report (451.82 KB)

A. Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., “Variational Optic Flow Computation in Real-Time”, Dept. Math. and Comp. Science, Saarland University, Germany, 89, 2003.

2004

A. Bruhn, Jakob, T., Fischer, M., Weickert, J., Brüning, U., and Schnörr, C., “High performance cluster computing with 3-D nonlinear diffusion filters”, Real-Time Imaging, vol. 10, pp. 41–51, 2004.

D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, in Computer Vision – ECCV 2004, 2004, vol. 3024, pp. 74-86.

2005

A. Bruhn, Weickert, J., Kohlberger, T., and Schnörr, C., “Discontinuity-Preserving Computation of Variational Optic Flow in Real-Time”, in Scale-Space 2005, 2005, vol. 3459, pp. 279–290.

A. Bruhn, Weickert, J., and Schnörr, C., “Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods”, vol. 61, pp. 211-231, 2005.

M. Welk, Becker, F., Schnörr, C., and Weickert, J., “Matrix-Valued Filters as Convex Programs”, in Scale-Space 2005, 2005, vol. 3459, pp. 204–216.

M. Bergtholdt and Schnörr, C., “Shape Priors and Online Appearance Learning for Variational Segmentation and Object Recognition in Static Scenes”, Pattern Recognition, Proc. 27th DAGM Symposium, vol. 3663. Springer, pp. 342–350, 2005.

J. Yuan, Schnörr, C., Steidl, G., and Becker, F., “A Study of Non-Smooth Convex Flow Decomposition”, in Proc. Variational, Geometric and Level Set Methods in Computer Vision, 2005, vol. 3752, pp. 1–12.

A. Bruhn, Weickert, J., Feddern, C., Kohlberger, T., and Schnörr, C., “Variational optic flow computation in real-time”, IEEE Trans. Image Proc., vol. 14, pp. 608–615, 2005.

P. Ruhnau, Kohlberger, T., Nobach, H., and Schnörr, C., “Variational Optical Flow Estimation for Particle Image Velocimetry”, Experiments in Fluids, vol. 38, p. 21--32, 2005.

Technical Report (1.21 MB)

M. Bergtholdt, Cremers, D., and Schnörr, C., “Variational Segmentation with Shape Priors”, Handbook of Mathematical Models in Computer Vision. Springer, pp. 147-160, 2005.

2006

S. Weber, Nagy, A., Schüle, T., Schnörr, C., and Kuba, A., “A Benchmark Evaluation of Large-Scale Optimization Approaches to Binary Tomography”, in Discrete Geometry for Computer Imagery (DGCI 2006), 2006, vol. 4245, pp. 146-156.

Technical Report (301.1 KB)

S. Weber, Schüle, T., Schnörr, C., and Kuba, A., “Binary Tomography with Deblurring”, in Combinatorial Image Analysis, 2006, vol. 4040, pp. 375-388.

Technical Report (803.63 KB)

M. Heiler and Schnörr, C., “Controlling Sparseness in Non-negative Tensor Factorization”, in Computer Vision -- ECCV 2006, 2006, vol. 3951, pp. 56-67.

Technical Report (568.86 KB)

A. Stahl, Ruhnau, P., and Schnörr, C., “A Distributed Parameter Approach to Dynamic Image Motion”, in ECCV 2006, International Workshop on The Representation and Use of Prior Knowledge in Vision, 2006.

Technical Report (1.24 MB)

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