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--- teaching:wt23:seminar [2022/10/12 08:35]
jschwarz
+++ teaching:wt23:seminar [2022/11/04 12:53] (current)
jschwarz [Modalities]
@@ Line 1: / Line 1: @@
 ====== Riemannian Geometric Statistics in Medical Image Analysis (Seminar) ======
-If you are interested, please write a short e-mail to jonathan.schwarz@iwr.uni-heidelberg.de.
 ===== Registration =====
-Please write a mail to  jonathan.schwarz@iwr.uni-heidelberg.de  until October 31st, 2020 if you want to participate.
+Please write a mail to  jonathan.schwarz@iwr.uni-heidelberg.de  until October 31st, 2022 if you want to participate.
 ===== Modalities =====
-The Proseminar will take place every Friday from 4-6 pm starting in November.
+The Seminar will take place every Friday from 4-6 pm starting at the 4th of November.
@@ Line 36: / Line 36: @@
 You have to hand in a written summary of your topic the latest 7 days after your talk.
   * The summary should be created with LaTeX and should be handed in as a Pdf file.
-  * I recommend the [[https://www.springer.com/de/it-informatik/lncs/conference-proceedings-guidelines|LNCS LaTeX Template]], but you don't have to use it.
   * The summary should be between 2 and 4 pages long
@@ Line 49: / Line 48: @@
 ==== Paper ====
-The Seminar will be based on the book **Riemannian Geometric Statistics in Medical Image Analysis**
+Part of the seminar will be based on the book **Riemannian Geometric Statistics in Medical Image Analysis** by Xavier Pennec, Stefan Sommer and Tom Fletcher. You will get access to the book.
-    * **SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering** by Kuang, Yun and Park, Journal of Global Optimization, 2015, {{ :teaching:st20:seminarmabi:kuang2015_sym_nmf.pdf |pdf}}
+Possible topics are:
-    *  **k-MLE: A fast algorithm for learning statistical mixture models** by Frank Nielsen, arXiv preprint, 2012, {{ :teaching:st20:seminarmabi:nielsen2012_k-mle.pdf |pdf}}
+  * **Manifold-valued image processing with SPD matrices**
-    * **Wasserstein Dictionary Learning: Optimal Transport-Based Unsupervised
+  * **Riemannian geometry on shapes and diffeomorphisms**
-Nonlinear Dictionary Learning** by Schmitz et al., SIAM Journal on Imaging Sciences, 2018, {{ :teaching:st20:seminarmabi:schmitz2018_wasserstein_dictionary.pdf |pdf}}
+  * **Beyond Riemannian geometry**
-    * **Ising and Potts models on the hypercubic lattice** by Duminil-Copin H., arXiv preprint arXiv:1707.00520, 2017 [[https://arxiv.org/abs/1707.00520|link]] (only one of the chapters 1, 2, 4, 5.1, 6.1)
+  * **Low-dimensional shape analysis in the space of diffeomorphisms**
-    * **Information geometry connecting Wasserstein distance and Kullback–Leibler divergence via the entropy-relaxed transportation problem** by Shun-ichi Amari et. al., Information Geometry, Springer, 2018, {{:teaching:ft1819:seminar:amari2018wasserstein.pdf|pdf}}
-    * **Escort Evolutionary Game Theory** by Marc Harper, arXiv, 2012, {{:teaching:ft1819:seminar:harper2011escort.pdf|pdf}}
+Possible other topics are:
+  * **Design and Processing of invertible orientation scores of 3D Images** [[https://link.springer.com/article/10.1007/s10851-018-0806-0|link]]
+  * **Locally Adaptive Frames in the Roto-Translation Group and Their Application in Medical Imaging** [[https://link.springer.com/article/10.1007/s10851-016-0641-0|link]]
+If there are any questions, please don't hesitate to ask and send an e-mail to  jonathan.schwarz@iwr.uni-heidelberg.de.