H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 38 Citations 7,838 204 World Ranking 4879 National Ranking 224

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Gene
  • Computer vision

Karl Rohr mainly focuses on Artificial intelligence, Computer vision, Image, Algorithm and Landmark. His work deals with themes such as Point and Pattern recognition, which intersect with Artificial intelligence. The Image registration, Segmentation and Motion research Karl Rohr does as part of his general Computer vision study is frequently linked to other disciplines of science, such as Property, therefore creating a link between diverse domains of science.

His Image study incorporates themes from Intensity, Mathematical analysis and Position. He has included themes like k-nearest neighbors algorithm, Fluorescence microscope, Ground truth, Corner detection and Topology in his Algorithm study. His Landmark research is multidisciplinary, incorporating perspectives in Spline and Thin plate spline.

His most cited work include:

  • Objective comparison of particle tracking methods (548 citations)
  • Recruitment and activation of a lipid kinase by hepatitis C virus NS5A is essential for integrity of the membranous replication compartment (390 citations)
  • Recruitment and activation of a lipid kinase by hepatitis C virus NS5A is essential for integrity of the membranous replication compartment (390 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Artificial intelligence, Computer vision, Image, Segmentation and Pattern recognition. His studies deal with areas such as Fluorescence microscope and Microscopy as well as Artificial intelligence. The Computer vision study which covers Algorithm that intersects with Regular polygon.

His Image study combines topics in areas such as Intensity, Mathematical analysis and Position. His work deals with themes such as Aortic arch and Robustness, which intersect with Segmentation. Karl Rohr has researched Image registration in several fields, including Image processing and Thin plate spline.

He most often published in these fields:

  • Artificial intelligence (60.06%)
  • Computer vision (46.93%)
  • Image (19.83%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (60.06%)
  • Computer vision (46.93%)
  • Pattern recognition (17.04%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Deep learning and Segmentation. His Artificial intelligence research is multidisciplinary, incorporating elements of Human brain and Microscopy. His Computer vision research is multidisciplinary, incorporating perspectives in Spline, Digital image correlation and Mitotic cell.

His Pattern recognition research is multidisciplinary, relying on both Breast cancer, Rat brain and Histopathology. In his work, Convex optimization, Image noise, Image segmentation, Optical flow and Feature is strongly intertwined with Algorithm, which is a subfield of Segmentation. His Image registration study incorporates themes from Image processing, Live cell imaging, Nucleus and System of linear equations.

Between 2016 and 2021, his most popular works were:

  • An objective comparison of cell-tracking algorithms (210 citations)
  • Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge. (82 citations)
  • Clathrin-adaptor ratio and membrane tension regulate the flat-to-curved transition of the clathrin coat during endocytosis. (67 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Gene
  • Computer vision

Karl Rohr focuses on Artificial intelligence, Computer vision, Biophysics, Artificial neural network and Cell biology. When carried out as part of a general Artificial intelligence research project, his work on Fast marching method is frequently linked to work in Path, therefore connecting diverse disciplines of study. His Point research extends to Computer vision, which is thematically connected.

Karl Rohr works mostly in the field of Artificial neural network, limiting it down to concerns involving Deep learning and, occasionally, Tracking, Fluorescence microscope and Pattern recognition. His Cell biology research includes elements of Mutant and Bacillus subtilis, Bacteria. His research investigates the connection with Image registration and areas like Live cell imaging which intersect with concerns in Image processing.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Objective comparison of particle tracking methods

Nicolas Chenouard;Ihor Smal;Fabrice de Chaumont;Martin Maška;Martin Maška.
Nature Methods (2014)

712 Citations

Towards model-based recognition of human movements in image sequences

K. Rohr.
Cvgip: Image Understanding (1994)

636 Citations

Landmark-based elastic registration using approximating thin-plate splines

K. Rohr;H.S. Stiehl;R. Sprengel;T.M. Buzug.
IEEE Transactions on Medical Imaging (2001)

571 Citations

Recruitment and activation of a lipid kinase by hepatitis C virus NS5A is essential for integrity of the membranous replication compartment

Simon Reiss;Ilka Rebhan;Perdita Backes;Ines Romero-Brey.
Cell Host & Microbe (2011)

472 Citations

A benchmark for comparison of cell tracking algorithms

Martin Maška;Vladimír Ulman;David Svoboda;Pavel Matula.
Bioinformatics (2014)

330 Citations

Landmark-Based Image Analysis: Using Geometric and Intensity Models

Karl Rohr.
(2010)

325 Citations

Chromatin domains and the interchromatin compartment form structurally defined and functionally interacting nuclear networks

Heiner Albiez;Marion Cremer;Cinzia Tiberi;Lorella Vecchio.
Chromosome Research (2006)

278 Citations

Radial basis functions with compact support for elastic registration of medical images

M. Fornefett;K. Rohr;H.S. Stiehl.
Image and Vision Computing (2001)

268 Citations

An objective comparison of cell-tracking algorithms

Vladimír Ulman;Martin Maška;Klas E G Magnusson;Olaf Ronneberger.
Nature Methods (2017)

251 Citations

Point-Based Elastic Registration of Medical Image Data Using Approximating Thin-Plate Splines

Karl Rohr;H. Siegfried Stiehl;Rainer Sprengel;Wolfgang Beil.
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing (1996)

237 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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