Publikationen

Zeitschriftenartikel

R. Machhamer, L. Begic Fazlic, E. Guven, D. Junk, G. Karabulut Kurt, S. Naumann, S. Didas, K.-U. Gollmer, R. Bergmann, I. J. Timm, G. Dartmann, Likelihood-Based Sensor Calibration Using Affine Transformation, IEEE Sensors, 24 (3), 3672-3680, 2023.
K. Scherer, S. Bast, J. Murach, S. Didas, G. Dartmann, M. Wahl: Nachhaltige und intelligente additive Fertigung, Industrie 4.0 Management 39 (2), 56-59, 2023.
S. G. Zadeh, S. Didas, M. W. M. Wintergerst, T. Schultz: Multi-Scale Anisotropic Fourth-Order Diffusion Improves Ridge and Valley Localization. Journal of Mathematical Imaging and Vision October 2017, Volume 59, Issue 2, pp 257–269.
M. K. Ahmad, S. Didas, A. Hasanov, J. Iqbal: Sharp operator based edge detection. Journal of Signal and Information Processing 6 (2), 180-189, 2015.
A. Pfrang, S. Didas, G. Tsotridis: X-ray computed tomography of gas diffusion layers of PEM fuel cells: Segmentation of the microporous layer. Journal of Power Sources 235, 81-86, 2013.
L. Pizarro, P. Mrázek, S. Didas, S. Grewenig, J. Weickert: Generalised nonlocal image smoothing. International Journal of Computer Vision 90 (1), 62-87, 2010.
S. Didas, G. Steidl, J. Weickert: Integrodifferential equations for multiscale wavelet shrinkage: the discrete case.International Journal of Electrical and Computer Engineering Systems 1 (1), 1-17, 2010.
M. Mickler, S. Didas, M. Rauhut, H.-J. Bart: In-Line Bildanalyse von Blasen- und Tropfenströmungen.Chemie Ingenieur Technik 81 (8), 1076-1077, 2009.
S. Didas, J. Weickert, B. Burgeth: Properties of Higher Order Nonlinear Diffusion Filtering.Journal of Mathematical Imaging and Vision 35 (3), 208-226, 2009.
S. Didas, S. Setzer, G. Steidl: Combined l_2 data and gradient fitting in conjunction with l_1 regularization. Advances in Computational Mathematics, 30 (1), 79-99, 2008.
B. Burgeth, S. Didas, L. Florack, J. Weickert: A generic approach to diffusion filtering of matrix-fields. Computing, Vol. 81, 179-197, 2007.
S. Didas, J. Weickert: Integrodifferential Equations for Continuous Multiscale Wavelet Shrinkage.Inverse Problems and Imaging, 1 (1), 47-62, 2007.
B. Burgeth, A. Bruhn, S. Didas, J. Weickert, M. Welk: Morphology for tensor data: Ordering versus PDE-based approach.Image and Vision Computing, 25 (4), 496-511, April 2007.
G. Steidl, S. Didas, J. Neumann: Splines in higher order TV regularization.International Journal of Computer Vision, 70 (3), 241-255, 2006.
N. Papenberg, A. Bruhn, T. Brox, S. Didas, J. Weickert: Highly accurate optic flow computation with theoretically justified warping.International Journal of Computer Vision, 67 (2), 141-158, April 2006.

Buchkapitel

S. Didas: Fundamentals of Data Analysis and Statistics. In G. Dartmann, H. Song, A. Schmeink (Eds.): Big Data Analytics for Cyber-Physical Systems - Machine Learning for the Internet of Things, Elsevier, 2019.
B. Burgeth, L. Pizarro, S. Didas, J. Weickert: 3D-coherence-enhancing diffusion filtering for matrix fields. In L. Florack, R. Duits, G. Jongbloed, M.-C. von Lieshout, L. Davies: Mathematical Methods for Signal and Image Analysis and Representation, Computational Imaging and Vision 41, Springer, Berlin, 2012.
J. Franke, R. Dahlhaus, J. Polzehl, V. Spokoiny, G. Steidl, J. Weickert, A. Berdychevski, S. Didas, S. Halim, P. Mrázek, S. S. Rao, J. Tadjuidje: Structural adaptive smoothing procedures. In R. Dahlhaus, J. Kurths, P. Maass, J. Timmer (Eds.): Mathematical Methods in Time Series Analysis and Digital Image Processing, Springer, Berlin, 2008.

Konferenzbeiträge

B. Burgeth, S. Didas, A. Kleefeld: A Unified Approach to the Processing of Hyperspectral Images. In B. Burgeth, A. Kleefeld, B. Naegel, N. Passat, B. Perret (Eds.): Mathematical Morphology and Its Applications to Signal and Image Processing, Springer International Publishing, 202-214, 2019.
H. Altendorf, S. Didas, T. Batt: Automatische Bestimmung von Faserradienverteilungen. In F. Puente Leon, M. Heizmann (Eds.): Forum Bildverarbeitung [pdf], Karlsruhe: KIT Scientific Publishing, 2010.
B. Burgeth, M. Breuß, S. Didas, J. Weickert: PDE-based morphology for matrix fields: numerical solution schemes. In S. Aja-Fernandez, R. de Luis-Garcia, D. Tao, X. Li (Eds.): Tensors in Image Processing and Computer Vision, 125-150, Springer, London, 2009.
B. Burgeth, S. Didas, J. Weickert: A general structure tensor concept and coherence-enhancing diffusion filtering for matrix fields. In D. H. Laidlaw, J. Weickert (Eds.) Visualization and Processing of Tensor Fields: Advances and Perspectives. Springer, Berlin, 305-323, 2009.
L. Pizarro, B. Burgeth, S. Didas, J. Weickert: A generic neighbourhood filtering framework for matrix fields. In D. Forsyth, P. Torr, A. Zisserman: Computer Vision - ECCV 2008, Lecture Notes in Computer Science, Vol. 5304, 521-532, 2008.
S. Zimmer, S. Didas, J. Weickert: A rotationally invariant block matching strategy improving image denoising with non-local means. Proc. 2008 International Workshop on Local and Non-Local Approximation in Image Processing (August 2008, Lausanne, Switzerland), in print.
S. Didas, G. Steidl, J. Weickert: Discrete multiscale wavelet shrinkage and integrodifferential equations. In P. Schelkens, T. Ebrahimi, G. Christobal, F. Truchetet, Optical and Digital Image Processing - Photonics Europe, Vol. 7000 of Proceedings of SPIE, 70000S-1 - 70000S-12, 2008.
L. Pizarro, S. Didas, F. Bauer, J. Weickert: Evaluating a general class of filters for image denoising. In B. K. Ersboll, K. S. Pedersen (Eds.): Image Analysis, Lecture Notes in Computer Science, Vol. 4522, 601 - 610, Springer, Berlin, 2007.
S. Didas, J. Weickert: Combining Curvature Motion and Edge-Preserving Denoising. In F. Sgallari, A. Murli, N. Paragios (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 4485, 568 - 579, Springer, Berlin, 2007.
O. Vogel, A. Bruhn, J. Weickert, S. Didas: Direct Shape-from-Shading with Adaptive Higher Order Regularisation. In F. Sgallari, A. Murli, N. Paragios (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 4485, 871 - 882, Springer, Berlin, 2007.
B. Burgeth, S. Didas, L. Florack, J. Weickert: Singular PDEs for the Processing of Matrix-Valued Data. In F. Sgallari, A. Murli, N. Paragios (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 4485, 556 - 567, Springer, Berlin, 2007.
S. Didas, P. Mrázek, J. Weickert: Energy-based image simplification with nonlocal data and smoothness terms. In A. Iske, J. Levesley (Eds.): Algorithms for Approximation, 50-60, Springer, Heidelberg, 2006.
S. Didas, J. Weickert: From adaptive averaging to accelerated nonlinear diffusion filtering. K. Franke et al. (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 4174, 101 - 110, Springer, Berlin, 2006.
B. Burgeth, S. Didas, J. Weickert: The Bessel scale-space. In O. F. Olsen, L. Florack, A. Kuijper (Eds.): Deep Structure, Singularities, and Computer Vision. Lecture Notes in Computer Science, Vol. 3753, 84 - 95, Springer, Berlin, 2005.
S. Didas, J. Weickert, B. Burgeth: Stability and local feature enhancement of higher order nonlinear diffusion filtering. In W. Kropatsch, R. Sablatnig and A. Hanbury (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 3663, Springer, Berlin, 2005.
S. Didas, B. Burgeth, A. Imiya, J. Weickert: Regularity and scale-space properties of fractional high order linear filtering. In R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale-Space and PDE Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 3459, Springer, Berlin, 2005.
B. Burgeth, S. Didas, J. Weickert: Relativistic scale-spaces. In R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale-Space and PDE Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 3459, Springer, Berlin, 2005.
G. Steidl, S. Didas, J. Neumann: Relations between higher order TV regularization and support vector regression. In R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale-Space and PDE Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 3459, Springer, Berlin, 2005.

Abschlussarbeiten

S. Didas:
Denoising and enhancement of digital images - variational methods, integrodifferential equations, and wavelets, PhD thesis, Dept. of Mathematics, Saarland University, 2008.
S. Didas:
Higher order variational methods for noise removal in signals and images, Diplomarbeit, Dept. of Mathematics, Saarland University, 2004.
S. Didas:
Synchronisation in the Network-Integrated Multimedia Middleware (NMM), Fortgeschrittenenpraktikum, Dept. of Computer Science, Saarland University, 2002, accepted as Bachelor's thesis, 2004.

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