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.
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.
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.
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.
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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)
Ilastik: Interactive learning and segmentation toolkit
Christoph Sommer;Christoph Straehle;Ullrich Kothe;Fred A. Hamprecht.
international symposium on biomedical imaging (2011)
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)
ilastik: interactive machine learning for (bio)image analysis.
Stuart Berg;Dominik Kutra;Thorben Kroeger;Christoph N Straehle.
Nature Methods (2019)
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)
Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices.
Hugo Kubinyi;Fred A. Hamprecht;Thomas Mietzner.
Journal of Medicinal Chemistry (1998)
An objective comparison of cell-tracking algorithms
Vladimír Ulman;Martin Maška;Klas E G Magnusson;Olaf Ronneberger.
Nature Methods (2017)
On oblique random forests
Bjoern H. Menze;B. Michael Kelm;Daniel N. Splitthoff;Ullrich Koethe.
european conference on machine learning (2011)
Learning to count with regression forest and structured labels
Luca Fiaschi;Ullrich Koethe;Rahul Nair;Fred A. Hamprecht.
international conference on pattern recognition (2012)
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)
Profile was last updated on December 6th, 2021.
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