H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 79 Citations 33,722 203 World Ranking 486 National Ranking 29

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Random forest. Artificial intelligence is closely attributed to Machine learning in his study. His Computer vision research incorporates themes from Discriminative model and Affine transformation.

His Pattern recognition research is multidisciplinary, relying on both Contextual image classification, Image retrieval and Medical imaging. His Segmentation research integrates issues from Convolutional neural network and Conditional random field. His work is dedicated to discovering how Random forest, Voxel are connected with Anatomy and other disciplines.

His most cited work include:

  • Region filling and object removal by exemplar-based image inpainting (2260 citations)
  • The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (1985 citations)
  • TextonBoost : joint appearance, shape and context modeling for multi-class object recognition and segmentation (1067 citations)

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

Antonio Criminisi mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Machine learning. His is doing research in Random forest, Image, Discriminative model, Image segmentation and Convolutional neural network, both of which are found in Artificial intelligence. His research integrates issues of Geodesic and Computer graphics in his study of Computer vision.

The study incorporates disciplines such as Camera auto-calibration and Camera resectioning in addition to Computer graphics. In general Pattern recognition, his work in Classifier is often linked to Set linking many areas of study. His work deals with themes such as Supervised learning, Ground truth and Conditional random field, which intersect with Segmentation.

He most often published in these fields:

  • Artificial intelligence (79.70%)
  • Computer vision (49.45%)
  • Pattern recognition (27.68%)

What were the highlights of his more recent work (between 2013-2021)?

  • Artificial intelligence (79.70%)
  • Pattern recognition (27.68%)
  • Convolutional neural network (8.49%)

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

His primary scientific interests are in Artificial intelligence, Pattern recognition, Convolutional neural network, Machine learning and Computer vision. His study in Deep learning, Artificial neural network, Segmentation, Discriminative model and Random forest is carried out as part of his Artificial intelligence studies. His research in Segmentation intersects with topics in Supervised learning and Standard test image.

His work on Training set as part of general Pattern recognition research is frequently linked to Set and Brain tumor, thereby connecting diverse disciplines of science. His study looks at the intersection of Machine learning and topics like Robustness with Linear programming and Overfitting. His study focuses on the intersection of Computer vision and fields such as Session with connections in the field of Perception and Usability.

Between 2013 and 2021, his most popular works were:

  • The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (1985 citations)
  • Unsupervised domain adaptation in brain lesion segmentation with adversarial networks (272 citations)
  • Deep Neural Decision Forests (221 citations)

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

  • Artificial intelligence
  • Computer vision
  • Machine learning

His primary areas of study are Artificial intelligence, Convolutional neural network, Artificial neural network, Pattern recognition and Segmentation. His Artificial intelligence study combines topics in areas such as Machine learning and Computer vision. His work on Image retrieval, Monocular and Augmented reality is typically connected to Close range as part of general Computer vision study, connecting several disciplines of science.

His studies deal with areas such as Linear programming, Scale-space segmentation and Transfer of learning as well as Artificial neural network. His work in the fields of Pattern recognition, such as Discriminative model and Image segmentation, intersects with other areas such as Brain tumor. His biological study spans a wide range of topics, including Interior design, Dilation, Computer graphics and Feature.

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

Region filling and object removal by exemplar-based image inpainting

A. Criminisi;P. Perez;K. Toyama.
IEEE Transactions on Image Processing (2004)

3317 Citations

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Bjoern H. Menze;Andras Jakab;Stefan Bauer;Jayashree Kalpathy-Cramer.
IEEE Transactions on Medical Imaging (2015)

1848 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

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

Object removal by exemplar-based inpainting

A. Criminisi;P. Perez;K. Toyama.
computer vision and pattern recognition (2003)

1199 Citations

Object categorization by learned universal visual dictionary

J. Winn;A. Criminisi;T. Minka.
international conference on computer vision (2005)

1062 Citations

Epitomic location recognition

Kai Ni;A. Kannan;A. Criminisi;J. Winn.
computer vision and pattern recognition (2008)

1050 Citations

Single View Metrology

A. Criminisi;I. Reid;A. Zisserman.
International Journal of Computer Vision (2000)

998 Citations

Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning

Antonio Criminisi;Jamie Shotton;Ender Konukoglu.
(2012)

876 Citations

Decision Forests for Computer Vision and Medical Image Analysis

A. Criminisi;J. Shotton.
(2013)

663 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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