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Computer Science

D-Index
53
Citations
16934
World Ranking
4710
National Ranking
15

Overview

Christian Igel is affiliated with the University of Copenhagen in Denmark. Their research spans multiple fields, primarily focusing on computer science and environmental science.

Their main subfields of study include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Environmental Engineering
  • Global and Planetary Change
  • Nature and Landscape Conservation

Christian Igel's research covers various topics, including:

  • Remote Sensing and LiDAR Applications
  • Forest Ecology and Management
  • Remote Sensing in Agriculture
  • COVID-19 Diagnosis Using AI
  • Natural Language Processing Techniques
  • Speech Recognition and Synthesis
  • Forest Ecology and Biodiversity Studies

Their recent publications include:

  • "The Liver Tumor Segmentation Benchmark (LiTS)" (2022), published in Medical Image Analysis
  • "An unexpectedly large count of trees in the West African Sahara and Sahel" (2020), published in Nature
  • "Self-Supervised Speech Representation Learning: A Review" (2022), published in IEEE Journal of Selected Topics in Signal Processing
  • "U-Sleep: resilient high-frequency sleep staging" (2021), published in npj Digital Medicine
  • "Sub-continental-scale carbon stocks of individual trees in African drylands" (2023), published in Nature

Christian Igel frequently collaborates with several co-authors. Notable among them are:

  • Stefan Oehmcke
  • Ankit Kariryaa
  • Fabian Gieseke
  • Martin Brandt
  • Rasmus Fensholt

Their work has been disseminated across various publication venues, with multiple contributions to:

  • arXiv (Cornell University)
  • Research Square (Research Square)
  • Zenodo (CERN European Organization for Nuclear Research)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • KI - Künstliche Intelligenz

Best Publications

  • 2012 Special Issue: Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition

    J. Stallkamp;M. Schlipsing;J. Salmen;C. Igel

  • The German Traffic Sign Recognition Benchmark: A multi-class classification competition

    Johannes Stallkamp;Marc Schlipsing;Jan Salmen;Christian Igel

  • Detection of traffic signs in real-world images: The German traffic sign detection benchmark

    Sebastian Houben;Johannes Stallkamp;Jan Salmen;Marc Schlipsing

  • Covariance Matrix Adaptation for Multi-objective Optimization

    Christian Igel;Nikolaus Hansen;Stefan Roth

  • An Introduction to Restricted Boltzmann Machines

    Asja Fischer;Asja Fischer;Christian Igel

  • Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

    Adhish Prasoon;Kersten Petersen;Christian Igel;François Lauze

  • Evolutionary tuning of multiple SVM parameters

    Frauke Friedrichs;Christian Igel

  • Training restricted Boltzmann machines

    Asja Fischer;Christian Igel

  • Empirical evaluation of the improved Rprop learning algorithms

    Christian Igel;Michael Hüsken

  • Improving the Rprop Learning Algorithm

    Christian Igel;Michael Husken

  • Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring

    Michiel Kallenberg;Kersten Petersen;Mads Nielsen;Andrew Y. Ng

  • Self-Supervised Speech Representation Learning: A Review

    Unknown

  • U-Sleep: resilient high-frequency sleep staging

    Mathias Perslev;Sune Darkner;Lykke Kempfner;Miki Nikolic

  • Early detection of Alzheimer's disease using MRI hippocampal texture

    Lauge Sørensen;Christian Igel;Naja Liv Hansen;Merete Osler

  • A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies

    Christian Igel;Thorsten Suttorp;Nikolaus Hansen

  • Neuroevolution for reinforcement learning using evolution strategies

    C. Igel

  • Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry

    Lauge Sørensen;Christian Igel;Akshay Pai;Ioana Balas

  • Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search

    Verena Heidrich-Meisner;Christian Igel

  • Image processing and behavior planning for intelligent vehicles

    T. Bucher;C. Curio;J. Edelbrunner;C. Igel

  • U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging

    Mathias Perslev;Michael Hejselbak Jensen;Sune Darkner;Poul Jørgen Jennum

  • A No-Free-Lunch Theorem for Non-Uniform Distributions of Target Functions

    Christian Igel;Marc Toussaint

  • Active learning with support vector machines

    Jan Kremer;Kim Steenstrup Pedersen;Christian Igel

Frequent Co-Authors

Mads Nielsen
Mads Nielsen University of Copenhagen
Marc Toussaint
Marc Toussaint Technical University of Berlin
Nikolaus Hansen
Nikolaus Hansen École Polytechnique
Marco Loog
Marco Loog Radboud University
Marc Modat
Marc Modat King's College London
Marleen de Bruijne
Marleen de Bruijne Erasmus University Rotterdam
Sebastien Ourselin
Sebastien Ourselin King's College London
Tom Heskes
Tom Heskes Radboud University
Ulas Bagci
Ulas Bagci Northwestern University
Gregor Schöner
Gregor Schöner Ruhr University Bochum

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