H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 32 Citations 7,957 223 World Ranking 7093 National Ranking 306

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Segmentation. His Artificial intelligence study frequently draws connections to adjacent fields such as Machine learning. He combines subjects such as Histogram, Local binary patterns and Wiener filter with his study of Pattern recognition.

In general Computer vision, his work in Image segmentation and Visual inspection is often linked to Software deployment and Open research linking many areas of study. His research in Feature extraction intersects with topics in Bag-of-words model and Random projection. His Segmentation study incorporates themes from Image processing, Automatic processing, Pipeline and Pattern recognition.

His most cited work include:

  • Deep Learning for Generic Object Detection: A Survey (461 citations)
  • A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure (313 citations)
  • Texture Classification from Random Features (242 citations)

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

Paul Fieguth spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Image segmentation. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Machine learning. His study ties his expertise on Robustness together with the subject of Computer vision.

The various areas that he examines in his Pattern recognition study include Histogram, Image and Local binary patterns. Paul Fieguth has included themes like Kalman filter, Sampling, Mathematical optimization and Random field in his Algorithm study. His Feature extraction research incorporates elements of Contextual image classification and Feature.

He most often published in these fields:

  • Artificial intelligence (63.01%)
  • Computer vision (34.80%)
  • Pattern recognition (34.17%)

What were the highlights of his more recent work (between 2015-2021)?

  • Artificial intelligence (63.01%)
  • Computer vision (34.80%)
  • Pattern recognition (34.17%)

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

Paul Fieguth mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Artificial neural network and Machine learning. His Artificial intelligence study focuses mostly on Deep learning, Projector, Robustness, Image and Convolutional neural network. His work deals with themes such as Pixel and Generative grammar, which intersect with Image.

The Computer vision study combines topics in areas such as Brightness and Tomography. He is interested in Feature extraction, which is a field of Pattern recognition. His work in the fields of Artificial neural network, such as Deep neural networks, intersects with other areas such as Modern evolutionary synthesis and Network architecture.

Between 2015 and 2021, his most popular works were:

  • Deep Learning for Generic Object Detection: A Survey (461 citations)
  • Median Robust Extended Local Binary Pattern for Texture Classification (200 citations)
  • Local binary features for texture classification (199 citations)

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

  • Artificial intelligence
  • Statistics
  • Computer vision

His main research concerns Artificial intelligence, Deep learning, Machine learning, Pattern recognition and Computer vision. He performs multidisciplinary study on Artificial intelligence and Process in his works. The concepts of his Deep learning study are interwoven with issues in Object detection and Projector.

His work on Reinforcement learning is typically connected to Prostate cancer as part of general Machine learning study, connecting several disciplines of science. His biological study deals with issues like Local binary patterns, which deal with fields such as Image texture. The Computer vision study combines topics in areas such as Visualization and Interpolation.

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

Deep Learning for Generic Object Detection: A Survey

Li Liu;Li Liu;Wanli Ouyang;Xiaogang Wang;Paul W. Fieguth.
International Journal of Computer Vision (2020)

601 Citations

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

Christian Koch;Kristina Georgieva;Varun Kasireddy;Burcu Akinci.
Advanced Engineering Informatics (2015)

467 Citations

Color-based tracking of heads and other mobile objects at video frame rates

P. Fieguth;D. Terzopoulos.
computer vision and pattern recognition (1997)

407 Citations

Texture Classification from Random Features

Li Liu;Paul Fieguth.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)

352 Citations

Median Robust Extended Local Binary Pattern for Texture Classification

Li Liu;Songyang Lao;Paul W. Fieguth;Yulan Guo.
IEEE Transactions on Image Processing (2016)

310 Citations

Median robust extended local binary pattern for texture classification

Li Liu;Paul Fieguth;Matti Pietikainen;Songyang Lao.
international conference on image processing (2015)

301 Citations

Extended local binary patterns for texture classification

Li Liu;Lingjun Zhao;Yunli Long;Gangyao Kuang.
Image and Vision Computing (2012)

299 Citations

Local binary features for texture classification

Li Liu;Paul Fieguth;Yulan Guo;Xiaogang Wang.
Pattern Recognition (2017)

296 Citations

Automated detection of cracks in buried concrete pipe images

Sunil K. Sinha;Paul W. Fieguth.
Automation in Construction (2006)

280 Citations

Adaptive Wiener filtering of noisy images and image sequences

F. Jin;P. Fieguth;L. Winger;E. Jernigan.
international conference on image processing (2003)

191 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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