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.
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.
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.
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.
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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)
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)
Color-based tracking of heads and other mobile objects at video frame rates
P. Fieguth;D. Terzopoulos.
computer vision and pattern recognition (1997)
Median Robust Extended Local Binary Pattern for Texture Classification
Li Liu;Songyang Lao;Paul W. Fieguth;Yulan Guo.
IEEE Transactions on Image Processing (2016)
Texture Classification from Random Features
Li Liu;Paul Fieguth.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Local binary features for texture classification
Li Liu;Paul Fieguth;Yulan Guo;Xiaogang Wang.
Pattern Recognition (2017)
Median robust extended local binary pattern for texture classification
Li Liu;Paul Fieguth;Matti Pietikainen;Songyang Lao.
international conference on image processing (2015)
Extended local binary patterns for texture classification
Li Liu;Lingjun Zhao;Yunli Long;Gangyao Kuang.
Image and Vision Computing (2012)
Automated detection of cracks in buried concrete pipe images
Sunil K. Sinha;Paul W. Fieguth.
Automation in Construction (2006)
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Li Liu;Li Liu;Jie Chen;Paul W. Fieguth;Guoying Zhao.
International Journal of Computer Vision (2019)
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