H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 55 Citations 17,639 242 World Ranking 2216 National Ranking 59

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

Radu Timofte spends much of his time researching Artificial intelligence, Computer vision, Image, Superresolution and Pattern recognition. Many of his studies on Artificial intelligence apply to Machine learning as well. His Computer vision research is multidisciplinary, incorporating perspectives in Pipeline and Pattern recognition.

His work on Compression is typically connected to Generator as part of general Image study, connecting several disciplines of science. His research investigates the connection between Superresolution and topics such as Image quality that intersect with problems in Benchmark and Contrast. His work is dedicated to discovering how Pattern recognition, Embedding are connected with Neural coding, Coding and Euclidean distance and other disciplines.

His most cited work include:

  • A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution (906 citations)
  • Anchored Neighborhood Regression for Fast Example-Based Super-Resolution (877 citations)
  • NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study (745 citations)

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

Radu Timofte focuses on Artificial intelligence, Computer vision, Image, Pattern recognition and Deep learning. His work on Superresolution, Image resolution and Convolutional neural network as part of general Artificial intelligence study is frequently linked to Focus, therefore connecting diverse disciplines of science. Many of his studies involve connections with topics such as Benchmark and Computer vision.

His Image research integrates issues from Process and Translation. In his work, Algorithm is strongly intertwined with Artificial neural network, which is a subfield of Pattern recognition. His study ties his expertise on Mobile device together with the subject of Deep learning.

He most often published in these fields:

  • Artificial intelligence (85.85%)
  • Computer vision (47.27%)
  • Image (31.83%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (85.85%)
  • Computer vision (47.27%)
  • Image (31.83%)

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

His primary scientific interests are in Artificial intelligence, Computer vision, Image, Deep learning and Superresolution. Radu Timofte has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition. His Computer vision study combines topics from a wide range of disciplines, such as Pipeline and Track.

His studies in Image integrate themes in fields like Code and Benchmark. His research integrates issues of Gaussian blur, Motion compensation and Rendering in his study of Deep learning. His Image restoration research incorporates themes from Multimedia, Convolutional neural network and Compression.

Between 2019 and 2021, his most popular works were:

  • Deep Unfolding Network for Image Super-Resolution (45 citations)
  • Probabilistic Regression for Visual Tracking (44 citations)
  • NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results (33 citations)

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

  • Artificial intelligence
  • Computer vision
  • Statistics

His scientific interests lie mostly in Artificial intelligence, Computer vision, Image, Superresolution and Set. Radu Timofte integrates Artificial intelligence and Focus in his studies. His Computer vision research focuses on Pipeline and how it relates to Image signal, Central processing unit and Frame rate.

His Image study deals with Structural similarity intersecting with Pipeline and Color balance. His work in Real image addresses issues such as Color space, which are connected to fields such as Pattern recognition and Noise reduction. Radu Timofte combines subjects such as Algorithm and Kernel with his study of Image resolution.

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.

Best Publications

A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution

Radu Timofte;Vincent De Smet;Luc J. Van Gool;Luc J. Van Gool.
asian conference on computer vision (2014)

1186 Citations

Anchored Neighborhood Regression for Fast Example-Based Super-Resolution

Radu Timofte;Vincent De;Luc Van Gool.
international conference on computer vision (2013)

979 Citations

NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study

Eirikur Agustsson;Radu Timofte.
computer vision and pattern recognition (2017)

857 Citations

Pedestrian detection at 100 frames per second

Rodrigo Benenson;Markus Mathias;Radu Timofte;Luc Van Gool.
computer vision and pattern recognition (2012)

693 Citations

NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte;Shuhang Gu;Luc Van Gool;Lei Zhang.
computer vision and pattern recognition (2018)

552 Citations

Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

Rasmus Rothe;Radu Timofte;Luc Van Gool.
International Journal of Computer Vision (2018)

512 Citations

NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

Radu Timofte;Eirikur Agustsson;Luc Van Gool;Ming-Hsuan Yang.
computer vision and pattern recognition (2017)

491 Citations

DEX: Deep EXpectation of Apparent Age from a Single Image

Rasmus Rothe;Radu Timofte;Luc Van Gool.
international conference on computer vision (2015)

443 Citations

Hough transform and 3D SURF for robust three dimensional classification

Jan Knopp;Mukta Prasad;Geert Willems;Radu Timofte.
european conference on computer vision (2010)

389 Citations

Multi-view traffic sign detection, recognition, and 3D localisation

Radu Timofte;Karel Zimmermann;Luc Van Gool.
machine vision applications (2014)

370 Citations

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