Christoph Schnörr

120 papers receiving 3.5k citations

Hit Papers

Lucas/Kanade meets Horn/Schunck: combining local and glob...20032026201020182003200400600

Peers

Christoph Schnörr
Comparison fields: 5 of 134
  • Computer Vision and Pattern Recognition 2.7k
  • Computational Mechanics 634
  • Artificial Intelligence 505
  • Radiology, Nuclear Medicine and Imaging 374
  • Biomedical Engineering 346
Replace Selim Esedoḡlu with:
Selim Esedoḡlu United States
Xavier Bresson Switzerland
Jayant Shah United States
Nikos Komodakis France
Gunnar Sparr Sweden
Michel Barlaud France
Gilles Aubert France
Fredrik Kahl Sweden
R. Malladi United States
Joachim Weickert Germany
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Citations per field
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Citations per year

Countries citing papers authored by Christoph Schnörr

Since Specialization
Citations

This map shows the geographic impact of Christoph Schnörr's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Christoph Schnörr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christoph Schnörr more than expected).

Fields of papers citing papers by Christoph Schnörr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christoph Schnörr. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Christoph Schnörr. The network helps show where Christoph Schnörr may publish in the future.

Co-authorship network of co-authors of Christoph Schnörr

This figure shows the co-authorship network connecting the top 25 collaborators of Christoph Schnörr. A scholar is included among the top collaborators of Christoph Schnörr based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Christoph Schnörr. Christoph Schnörr is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 9
2 0
3 12
4 33
5
Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation
7
6 16
7 26
8 5
9 24
10 9
11
Pattern recognition : 29th DAGM Symposium, Heidelberg, Germany, September 12-14, 2007 : proceedings
12
12
Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
56
13 27
14 13
15 96
16 1
17
Evaluation of Convex Optimization Techniques for the Weighted Graph-Matching Problem in Computer Vision
2
18
PDE-Based Preprocessing of Medical Images
14
19
Linear and incremental estimation of elastic deformations in medical registration using prescribed displacements
2
20 8

About Christoph Schnörr

Christoph Schnörr is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Biophysics, having authored 128 papers that have together received 3.7k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (46 papers), Advanced Vision and Imaging (28 papers) and Sparse and Compressive Sensing Techniques (25 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.7k citations), Computer Graphics and Computer-Aided Design (166 citations) and Media Technology (312 citations). Christoph Schnörr has collaborated with scholars based in Germany, France and United Kingdom. Frequent co-authors include Joachim Weickert, Andrés Bruhn, Daniel Cremers, Timo Kohlberger, Étienne Mémin, Gabriele Steidl, Jörg Hendrik Kappes, Julia Neumann, Jan Lellmann and Paul Ruhnau. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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