H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 80 Citations 35,913 231 World Ranking 431 National Ranking 18

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

Carsten Rother mainly investigates Artificial intelligence, Computer vision, Image segmentation, Segmentation and Pattern recognition. His Artificial intelligence research integrates issues from Machine learning and Markov chain. As a part of the same scientific study, Carsten Rother usually deals with the Computer vision, concentrating on Robustness and frequently concerns with Filter and Smoothing.

Carsten Rother has researched Image segmentation in several fields, including Stereopsis, Algorithm, Discriminative model and Edge detection. In his study, which falls under the umbrella issue of Segmentation, Iterative method and Simple interactive object extraction is strongly linked to Image editing. His research in Pattern recognition intersects with topics in Cognitive neuroscience of visual object recognition and Image retrieval.

His most cited work include:

  • "GrabCut": interactive foreground extraction using iterated graph cuts (4799 citations)
  • TextonBoost : joint appearance, shape and context modeling for multi-class object recognition and segmentation (1067 citations)
  • TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context (949 citations)

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

His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image. Artificial intelligence is closely attributed to Machine learning in his study. Carsten Rother has included themes like Computer graphics and Robustness in his Computer vision study.

His study in the fields of Conditional random field under the domain of Pattern recognition overlaps with other disciplines such as Task. The study incorporates disciplines such as Pascal and Convolutional neural network in addition to Segmentation. His studies in Cut integrate themes in fields like Algorithm and Markov chain.

He most often published in these fields:

  • Artificial intelligence (78.55%)
  • Computer vision (45.21%)
  • Pattern recognition (26.07%)

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

  • Artificial intelligence (78.55%)
  • Artificial neural network (13.20%)
  • Pattern recognition (26.07%)

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

Carsten Rother mainly focuses on Artificial intelligence, Artificial neural network, Pattern recognition, Robustness and Invertible matrix. His Artificial intelligence research incorporates elements of Machine learning and Computer vision. His Pattern recognition study combines topics in areas such as Deep learning and Multispectral image.

His Robustness research incorporates themes from Contextual image classification and Segmentation. His work on Image segmentation as part of general Segmentation study is frequently linked to Benchmarking, therefore connecting diverse disciplines of science. In Image segmentation, he works on issues like Pixel, which are connected to Task.

Between 2018 and 2021, his most popular works were:

  • Panoptic Segmentation (285 citations)
  • Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses (65 citations)
  • Guided Image Generation with Conditional Invertible Neural Networks (55 citations)

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

  • Artificial intelligence
  • Computer vision
  • Machine learning

Carsten Rother spends much of his time researching Artificial intelligence, Artificial neural network, Robustness, Pattern recognition and RANSAC. His Artificial intelligence study frequently links to adjacent areas such as Computer vision. His Robustness study incorporates themes from Segmentation, Pascal and Data mining.

His research integrates issues of Pixel and Metric in his study of Segmentation. His studies deal with areas such as Estimation theory, Posterior probability, Deep learning and Multispectral image as well as Pattern recognition. His study looks at the relationship between RANSAC and fields such as Robust statistics, as well as how they intersect with chemical problems.

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

"GrabCut": interactive foreground extraction using iterated graph cuts

Carsten Rother;Vladimir Kolmogorov;Andrew Blake.
international conference on computer graphics and interactive techniques (2004)

6404 Citations

TextonBoost : joint appearance, shape and context modeling for multi-class object recognition and segmentation

Jamie Shotton;John Winn;Carsten Rother;Antonio Criminisi.
european conference on computer vision (2006)

1421 Citations

A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors

R. Szeliski;R. Zabih;D. Scharstein;O. Veksler.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2008)

1340 Citations

TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

Jamie Shotton;John Winn;Carsten Rother;Antonio Criminisi.
International Journal of Computer Vision (2009)

1204 Citations

Fast Cost-Volume Filtering for Visual Correspondence and Beyond

A. Hosni;C. Rhemann;M. Bleyer;C. Rother.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)

1050 Citations

Fast cost-volume filtering for visual correspondence and beyond

Christoph Rhemann;Asmaa Hosni;Michael Bleyer;Carsten Rother.
computer vision and pattern recognition (2011)

825 Citations

Interactive Image Segmentation Using an Adaptive GMMRF Model

Andrew Blake;Carsten Rother;Matthew A. Brown;Patrick Pérez.
european conference on computer vision (2004)

754 Citations

Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs

C. Rother;T. Minka;A. Blake;V. Kolmogorov.
computer vision and pattern recognition (2006)

635 Citations

Optimizing Binary MRFs via Extended Roof Duality

C. Rother;V. Kolmogorov;V. Lempitsky;M. Szummer.
computer vision and pattern recognition (2007)

519 Citations

A comparative study of energy minimization methods for markov random fields

Richard Szeliski;Ramin Zabih;Daniel Scharstein;Olga Veksler.
european conference on computer vision (2006)

484 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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