H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 51 Citations 9,675 170 World Ranking 2726 National Ranking 123

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Fred A. Hamprecht mainly focuses on Artificial intelligence, Pattern recognition, Segmentation, Algorithm and Random forest. Fred A. Hamprecht has researched Artificial intelligence in several fields, including Machine learning and Computer vision. In his work, Univariate, Linear regression, Feature, Linear classifier and Partial least squares regression is strongly intertwined with Regression analysis, which is a subfield of Pattern recognition.

Fred A. Hamprecht combines subjects such as Image processing, Probabilistic logic, Rank and Mathematical optimization with his study of Segmentation. His study in Algorithm is interdisciplinary in nature, drawing from both Property, Manifold and Complement. His work carried out in the field of Random forest brings together such families of science as Classifier, Permutation, Feature and Feature vector.

His most cited work include:

  • Development and assessment of new exchange-correlation functionals (1117 citations)
  • Ilastik: Interactive learning and segmentation toolkit (744 citations)
  • ilastik: interactive machine learning for (bio)image analysis. (382 citations)

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

Artificial intelligence, Pattern recognition, Algorithm, Segmentation and Computer vision are his primary areas of study. His work is dedicated to discovering how Artificial intelligence, Machine learning are connected with Training set and other disciplines. His research on Pattern recognition frequently connects to adjacent areas such as Feature.

His Algorithm research is multidisciplinary, incorporating elements of Graph, Artificial neural network, Graphical model, Mathematical optimization and Bayesian probability. His Graphical model study combines topics from a wide range of disciplines, such as Probabilistic logic and Inference. As part of the same scientific family, Fred A. Hamprecht usually focuses on Segmentation, concentrating on Benchmark and intersecting with Signed graph and Structured prediction.

He most often published in these fields:

  • Artificial intelligence (60.17%)
  • Pattern recognition (34.85%)
  • Algorithm (20.75%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (60.17%)
  • Pattern recognition (34.85%)
  • Segmentation (19.09%)

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

Fred A. Hamprecht focuses on Artificial intelligence, Pattern recognition, Segmentation, Image and Benchmark. The study incorporates disciplines such as Machine learning and Graph partition in addition to Artificial intelligence. The concepts of his Pattern recognition study are interwoven with issues in Autoencoder, Noise and Inference.

He has included themes like Graph, Pixel, Inpainting and Algorithm, Greedy algorithm in his Segmentation study. His work investigates the relationship between Image and topics such as Semaphore that intersect with problems in Feature and Pyramid. His work in Convolutional neural network covers topics such as Image segmentation which are related to areas like Workflow, Object, Image processing and Process.

Between 2018 and 2021, his most popular works were:

  • ilastik: interactive machine learning for (bio)image analysis. (382 citations)
  • Accurate and versatile 3D segmentation of plant tissues at cellular resolution (25 citations)
  • Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures. (22 citations)

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

  • Artificial intelligence
  • Statistics
  • Machine learning

His primary scientific interests are in Artificial intelligence, Artificial neural network, Segmentation, Convolutional neural network and Pattern recognition. His Artificial intelligence research includes elements of Machine learning and Line. His Artificial neural network research is multidisciplinary, incorporating elements of Boosting, Heuristics and Graph partition.

As a part of the same scientific study, he usually deals with the Segmentation, concentrating on Benchmark and frequently concerns with Algorithm, Semaphore, Correlation clustering, Signed graph and Image. His study looks at the intersection of Convolutional neural network and topics like 3d segmentation with Graphical user interface. His research in Image segmentation intersects with topics in Process, Graph, Random walker algorithm, End-to-end principle and 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

Development and assessment of new exchange-correlation functionals

Fred A. Hamprecht;Aron J. Cohen;David J. Tozer;Nicholas C. Handy.
Journal of Chemical Physics (1998)

1583 Citations

Ilastik: Interactive learning and segmentation toolkit

Christoph Sommer;Christoph Straehle;Ullrich Kothe;Fred A. Hamprecht.
international symposium on biomedical imaging (2011)

973 Citations

A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

Bjoern H Menze;B Michael Kelm;Ralf Masuch;Uwe Himmelreich.
BMC Bioinformatics (2009)

513 Citations

ilastik: interactive machine learning for (bio)image analysis.

Stuart Berg;Dominik Kutra;Thorben Kroeger;Christoph N Straehle.
Nature Methods (2019)

382 Citations

A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems

Jorg H. Kappes;Bjoern Andres;Fred A. Hamprecht;Christoph Schnorr.
computer vision and pattern recognition (2013)

339 Citations

Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices.

Hugo Kubinyi;Fred A. Hamprecht;Thomas Mietzner.
Journal of Medicinal Chemistry (1998)

284 Citations

An objective comparison of cell-tracking algorithms

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

251 Citations

On oblique random forests

Bjoern H. Menze;B. Michael Kelm;Daniel N. Splitthoff;Ullrich Koethe.
european conference on machine learning (2011)

235 Citations

Learning to count with regression forest and structured labels

Luca Fiaschi;Ullrich Koethe;Rahul Nair;Fred A. Hamprecht.
international conference on pattern recognition (2012)

198 Citations

A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems

Jörg H. Kappes;Bjoern Andres;Fred A. Hamprecht;Christoph Schnörr.
International Journal of Computer Vision (2015)

186 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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