What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
Hans Knutsson mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Diffusion MRI.
The various areas that Hans Knutsson examines in his Artificial intelligence study include Machine learning and Noise.
His Computer vision research is multidisciplinary, incorporating perspectives in Quadrature and Orientation.
His studies deal with areas such as Magnetic resonance imaging, Colors of noise, Functional magnetic resonance imaging and Least squares as well as Pattern recognition.
Hans Knutsson combines subjects such as Network synthesis filters, Mathematical optimization, Log-normal distribution, Bandwidth and Signal with his study of Algorithm.
His biological study spans a wide range of topics, including Fiber, Diffusion, Covariance, Scalar and Voxel.
His most cited work include:
- Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates (2130 citations)
- Signal processing for computer vision (669 citations)
- Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004 (418 citations)
What are the main themes of his work throughout his whole career to date?
Hans Knutsson mostly deals with Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Adaptive filter.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Functional magnetic resonance imaging and Filter.
His Computer vision research includes elements of Phase and Computer graphics.
His Algorithm research includes themes of Tensor, Filter design, Quadrature, Mathematical optimization and Convolution.
Hans Knutsson studies Pattern recognition, focusing on Canonical correlation in particular.
He most often published in these fields:
- Artificial intelligence (41.84%)
- Computer vision (28.74%)
- Algorithm (15.86%)
What were the highlights of his more recent work (between 2011-2020)?
- Artificial intelligence (41.84%)
- Diffusion MRI (4.60%)
- Algorithm (15.86%)
In recent papers he was focusing on the following fields of study:
Hans Knutsson mainly focuses on Artificial intelligence, Diffusion MRI, Algorithm, Diffusion and Functional magnetic resonance imaging.
His studies in Artificial intelligence integrate themes in fields like Computer vision and Pattern recognition.
His research in Algorithm intersects with topics in Fiber, Tensor, Sampling, Fourier transform and Undersampling.
His Functional magnetic resonance imaging research incorporates elements of Resting state fMRI, Machine learning, Inference and Voxel.
His Inference research integrates issues from Nonparametric statistics, False positive paradox and Multiple comparisons problem.
Hans Knutsson has included themes like Parametric statistics and Permutation in his Nonparametric statistics study.
Between 2011 and 2020, his most popular works were:
- Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates (2130 citations)
- q-space trajectory imaging for multidimensional diffusion MRI of the human brain. (174 citations)
- Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets (99 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Geometry
His main research concerns Artificial intelligence, Diffusion MRI, Diffusion, Tensor and Functional magnetic resonance imaging.
His Artificial intelligence study integrates concerns from other disciplines, such as Monomial, Parametric statistics, Magnetic resonance imaging, Computer vision and Pattern recognition.
His research integrates issues of Skull, Focus and Support vector machine in his study of Computer vision.
His Diffusion MRI research incorporates themes from Wishart distribution, Mathematical analysis, Scalar and Topology.
The Tensor study combines topics in areas such as Prior probability, Markov chain Monte Carlo, Covariance, Scalar and Algorithm.
His Inference study combines topics in areas such as Resampling, Statistics, Spatial analysis, Permutation and Task analysis.
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