Daniel Schmitter

737 total citations
23 papers, 539 citations indexed

About

Daniel Schmitter is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Computer Graphics and Computer-Aided Design. According to data from OpenAlex, Daniel Schmitter has authored 23 papers receiving a total of 539 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 11 papers in Computational Mechanics and 5 papers in Computer Graphics and Computer-Aided Design. Recurrent topics in Daniel Schmitter's work include Advanced Numerical Analysis Techniques (11 papers), 3D Shape Modeling and Analysis (6 papers) and Medical Image Segmentation Techniques (5 papers). Daniel Schmitter is often cited by papers focused on Advanced Numerical Analysis Techniques (11 papers), 3D Shape Modeling and Analysis (6 papers) and Medical Image Segmentation Techniques (5 papers). Daniel Schmitter collaborates with scholars based in Switzerland, Germany and Italy. Daniel Schmitter's co-authors include Michaël Unser, Daniel Sage, Ricard Delgado-Gonzalo, Virginie Uhlmann, Marlen Knobloch, Marta Roccio, Matthias P. Lütolf, Meritxell Bach Cuadra, Alessandro Daducci and Alexis Roche and has published in prestigious journals such as Development, The Journal of Clinical Endocrinology & Metabolism and Journal of Cell Science.

In The Last Decade

Daniel Schmitter

23 papers receiving 533 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel Schmitter Switzerland 10 131 89 72 69 65 23 539
Frank Hertel Luxembourg 17 111 0.8× 43 0.5× 142 2.0× 24 0.3× 9 0.1× 58 1.1k
Jie Peng China 8 126 1.0× 36 0.4× 71 1.0× 17 0.2× 36 0.6× 12 647
G. Tavares Brazil 11 90 0.7× 55 0.6× 12 0.2× 28 0.4× 27 0.4× 22 478
Artem Khmelinskii Netherlands 9 121 0.9× 41 0.5× 140 1.9× 10 0.1× 8 0.1× 22 439
Bistra Iordanova United States 11 89 0.7× 120 1.3× 278 3.9× 17 0.2× 5 0.1× 24 792
Matthijs van Eede Canada 12 258 2.0× 29 0.3× 21 0.3× 7 0.1× 17 0.3× 14 579
Daniel J. Tward United States 21 153 1.2× 88 1.0× 904 12.6× 25 0.4× 14 0.2× 61 1.4k
Michael Merickel United States 17 108 0.8× 126 1.4× 231 3.2× 19 0.3× 9 0.1× 52 804
Dmitry D. Postnov Denmark 19 82 0.6× 19 0.2× 387 5.4× 13 0.2× 54 0.8× 51 898
Yiheng Xie United States 10 310 2.4× 183 2.1× 24 0.3× 109 1.6× 59 0.9× 11 1.2k

Countries citing papers authored by Daniel Schmitter

Since Specialization
Citations

This map shows the geographic impact of Daniel Schmitter'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 Daniel Schmitter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Schmitter more than expected).

Fields of papers citing papers by Daniel Schmitter

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Schmitter. 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 Daniel Schmitter. The network helps show where Daniel Schmitter may publish in the future.

Co-authorship network of co-authors of Daniel Schmitter

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Schmitter. A scholar is included among the top collaborators of Daniel Schmitter 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 Daniel Schmitter. Daniel Schmitter 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
1.
Schmitter, Daniel, et al.. (2025). Exact Implementation of Closed-Form Liquid Neural Networks With Arbitrary Precision. IEEE Signal Processing Letters. 32. 921–925. 2 indexed citations
2.
Lehman, Clarence, et al.. (2020). Unifying the Basic Models of Ecology to Be More Complete and Easier to Teach. BioScience. 70(5). 415–426. 5 indexed citations
3.
Schmitter, Daniel & Michaël Unser. (2017). Landmark-Based Shape Encoding and Sparse-Dictionary Learning in the Continuous Domain. IEEE Transactions on Image Processing. 27(1). 365–378. 2 indexed citations
4.
Schmitter, Daniel, et al.. (2017). Smooth shapes with spherical topology: Beyond traditional modeling, efficient deformation, and interaction. Computational Visual Media. 3(3). 199–215. 4 indexed citations
5.
Romani, Lucia, et al.. (2017). A non-stationary subdivision scheme for the construction of deformable models with sphere-like topology. Graphical Models. 94. 38–51. 16 indexed citations
6.
Schmitter, Daniel, et al.. (2016). An Inner-Product Calculus for Periodic Functions and Curves. IEEE Signal Processing Letters. 23(6). 878–882. 6 indexed citations
7.
Schmitter, Daniel, et al.. (2016). Multiresolution Subdivision Snakes. IEEE Transactions on Image Processing. 26(3). 1188–1201. 17 indexed citations
8.
Schmitter, Daniel, et al.. (2016). Smoothly deformable spheres. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1–4. 2 indexed citations
9.
Granziera, Cristina, Alessandro Daducci, Alessia Donati, et al.. (2015). A multi-contrast MRI study of microstructural brain damage in patients with mild cognitive impairment. NeuroImage Clinical. 8. 631–639. 20 indexed citations
10.
Schmitter, Daniel, Ricard Delgado-Gonzalo, & Michaël Unser. (2015). A family of smooth and interpolatory basis functions for parametric curve and surface representation. Applied Mathematics and Computation. 272. 53–63. 6 indexed citations
11.
Schmitter, Daniel & Michaël Unser. (2015). Similarity-based shape priors for 2D spline snakes. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 9. 1216–1219. 2 indexed citations
12.
Delgado-Gonzalo, Ricard, Daniel Schmitter, Virginie Uhlmann, & Michaël Unser. (2015). Efficient Shape Priors for Spline-Based Snakes. IEEE Transactions on Image Processing. 24(11). 3915–3926. 14 indexed citations
13.
Schmitter, Daniel, et al.. (2015). Locally refinable parametric snakes. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 354–358. 2 indexed citations
14.
Schmitter, Daniel, Ricard Delgado-Gonzalo, & Michaël Unser. (2015). Trigonometric Interpolation Kernel to Construct Deformable Shapes for User-Interactive Applications. IEEE Signal Processing Letters. 22(11). 2097–2101. 6 indexed citations
15.
Schmitter, Daniel, Alexis Roche, Bénédicte Maréchal, et al.. (2014). An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease. NeuroImage Clinical. 7. 7–17. 154 indexed citations
16.
Chasapi, Anastasia, Andrea Krapp, Daniel Schmitter, et al.. (2014). Analysis ofS. pombeSIN protein SPB-association reveals two genetically separable states of the SIN. Journal of Cell Science. 128(4). 741–54. 11 indexed citations
17.
Delgado-Gonzalo, Ricard, Virginie Uhlmann, Daniel Schmitter, & Michaël Unser. (2014). Snakes on a Plane: A perfect snap for bioimage analysis. IEEE Signal Processing Magazine. 32(1). 41–48. 50 indexed citations
18.
Schmitter, Daniel, Daniel Sage, Anastasia Chasapi, et al.. (2013). A 2D/3D image analysis system to track fluorescently labeled structures in rod-shaped cells: application to measure spindle pole asymmetry during mitosis. Cell Division. 8(1). 6–6. 12 indexed citations
19.
Mannic, Tiphaine, Patrick Meyer, Frédéric Triponez, et al.. (2013). Circadian Clock Characteristics Are Altered in Human Thyroid Malignant Nodules. The Journal of Clinical Endocrinology & Metabolism. 98(11). 4446–4456. 81 indexed citations
20.
Roccio, Marta, et al.. (2012). Predicting stem cell fate changes by differential cell cycle progression patterns. Development. 140(2). 459–470. 117 indexed citations

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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