What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Video tracking, Computer vision, Eye tracking and Machine learning.
Much of his study explores Artificial intelligence relationship to Pattern recognition.
His Video tracking study contributes to a more complete understanding of Tracking.
His work deals with themes such as Advanced driver assistance systems and Sign, which intersect with Computer vision.
In his study, Correlation filter, Brute-force search, Scale space and Filter is strongly linked to Scale, which falls under the umbrella field of Eye tracking.
His Discriminative model research incorporates elements of Feature extraction and Convolution.
His most cited work include:
- Accurate scale estimation for robust visual tracking (1343 citations)
- Learning Spatially Regularized Correlation Filters for Visual Tracking (1024 citations)
- ECO: Efficient Convolution Operators for Tracking (972 citations)
What are the main themes of his work throughout his whole career to date?
Michael Felsberg spends much of his time researching Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Video tracking.
As part of his studies on Artificial intelligence, he often connects relevant subjects like Machine learning.
Computer vision is often connected to Visualization in his work.
His Algorithm study combines topics from a wide range of disciplines, such as Smoothing, Filter, Image processing, Orientation and Mathematical optimization.
His Pattern recognition study integrates concerns from other disciplines, such as Image, Representation and Feature.
His biological study spans a wide range of topics, including Convolution and Active appearance model.
He most often published in these fields:
- Artificial intelligence (70.28%)
- Computer vision (40.96%)
- Algorithm (18.88%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (70.28%)
- Computer vision (40.96%)
- Machine learning (9.24%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Machine learning, Segmentation and Convolutional neural network are his primary areas of study.
Artificial intelligence is often connected to Pattern recognition in his work.
He has researched Computer vision in several fields, including Visualization and Signal processing.
Michael Felsberg has included themes like Object, Ground truth, Simple and Representation in his Segmentation study.
The various areas that Michael Felsberg examines in his Convolutional neural network study include Algorithm, Coding, Regression and Benchmark.
His Discriminative model study incorporates themes from Convolution, Filter, Eye tracking and Active appearance model.
Between 2016 and 2021, his most popular works were:
- ECO: Efficient Convolution Operators for Tracking (972 citations)
- Discriminative Scale Space Tracking (592 citations)
- The sixth visual object tracking VOT2018 challenge results (299 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Michael Felsberg mainly focuses on Artificial intelligence, Video tracking, Machine learning, Robustness and Convolutional neural network.
His Artificial intelligence research includes elements of Computer vision and Pattern recognition.
His Computer vision study typically links adjacent topics like Visualization.
The study incorporates disciplines such as Pascal, Coding and Image representation in addition to Pattern recognition.
His work deals with themes such as Convolution and Regression, which intersect with Convolutional neural network.
His work carried out in the field of Discriminative model brings together such families of science as Eye tracking, Filter and Brute-force search.
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.
