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L. Cohen

18 papers hereh-index 4211.7k citations259 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author17

Across the 18 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CV11
  • cs.CG4
  • cs.LG1
  • cs.RO1
  • eess.IV1
same name
  • L. Cohen — 60 papers, h 49
  • L. Cohen — 20 papers, h 19
  • L. Cohen — 18 papers, h 9
  • L. Cohen — 15 papers, h 17
  • L. Cohen — 7 papers, h 35
  • L. Cohen — 7 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162026
most citedGlobal Minimum for a Finsler Elastica Minimal Path Approach

51 citations · 153 across the 17 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CG2023

Computing Geodesic Paths Encoding a Curvature Prior

Da Chen, Jean-Marie Mirebeau, Minglei Shu +1

In this paper, we introduce an efficient method for computing curves minimizing a variant of the Euler-Mumford elastica energy, with fixed endpoints and tangents at these endpoints…

cs.CV2023★ 3 cited

Fast Marching Energy CNN

Nicolas Makaroff, Théo Bertrand, Laurent D. Cohen

Leveraging geodesic distances and the geometrical information they convey is key for many data-oriented applications in imaging. Geodesic distance computation has been used for lon…

eess.IV2023

Chan-Vese Attention U-Net: An attention mechanism for robust segmentation

Nicolas Makaroff, Laurent D. Cohen

When studying the results of a segmentation algorithm using convolutional neural networks, one wonders about the reliability and consistency of the results. This leads to questioni…

cs.LG2023★ 2 cited

A model is worth tens of thousands of examples

Thomas Dagès, Laurent D. Cohen, Alfred M. Bruckstein

Traditional signal processing methods relying on mathematical data generation models have been cast aside in favour of deep neural networks, which require vast amounts of data. Sin…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.