What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Geometry
- Computer vision
His primary areas of investigation include Artificial intelligence, Computer vision, Geometry, Algorithm and Motion estimation.
Kenichi Kanatani interconnects Motion and Pattern recognition in the investigation of issues within Artificial intelligence.
The Computer vision study combines topics in areas such as Differential geometry, Group and Lie algebra.
Kenichi Kanatani combines subjects such as Image processing and Normalization, Statistics with his study of Algorithm.
His research integrates issues of Mathematical analysis, Singular value decomposition, Quaternions and spatial rotation, Euler's rotation theorem and Matrix decomposition in his study of Motion estimation.
His Segmentation research includes elements of Point, Unsupervised learning and Feature.
His most cited work include:
- Statistical Optimization for Geometric Computation: Theory and Practice (484 citations)
- Geometric Computation for Machine Vision (382 citations)
- Motion segmentation by subspace separation and model selection (324 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Algorithm, Computation and Mathematical optimization are his primary areas of study.
Artificial intelligence and Pattern recognition are frequently intertwined in his study.
His Computer vision study combines topics from a wide range of disciplines, such as Motion and Point.
His Algorithm study combines topics in areas such as Ellipse, Image processing, Maximum likelihood, Statistics and Noise.
His Computation research is multidisciplinary, incorporating elements of Singular value decomposition and Fundamental matrix.
His Mathematical optimization course of study focuses on Applied mathematics and Covariance.
He most often published in these fields:
- Artificial intelligence (49.17%)
- Computer vision (35.55%)
- Algorithm (29.57%)
What were the highlights of his more recent work (between 2010-2021)?
- Artificial intelligence (49.17%)
- Computer vision (35.55%)
- Computation (19.27%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Computation, Algorithm and Algebra.
His Artificial intelligence study integrates concerns from other disciplines, such as Grid, Parallel and Collinearity.
His studies deal with areas such as Point and Metric as well as Computer vision.
His Computation research integrates issues from Ellipse, 3d vision, Rotation, Focus and Fundamental matrix.
His Algorithm research incorporates themes from Maximum likelihood, Sparse matrix and Mathematical optimization.
As part of one scientific family, Kenichi Kanatani deals mainly with the area of Algebra, narrowing it down to issues related to the Filtered algebra, and often Quaternion and Cellular algebra.
Between 2010 and 2021, his most popular works were:
- Hyper least squares fitting of circles and ellipses (78 citations)
- Calibration of Ultrawide Fisheye Lens Cameras by Eigenvalue Minimization (28 citations)
- Renormalization returns: hyper-renormalization and its applications (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Geometry
Kenichi Kanatani spends much of his time researching Artificial intelligence, Computer vision, Computation, Algorithm and Ellipse.
His Artificial intelligence research is multidisciplinary, relying on both Grid and Collinearity.
His research in Computer vision intersects with topics in Point and Parallel.
The concepts of his Computation study are interwoven with issues in Computer engineering, Multiple view, Bundle adjustment, 3d vision and Fundamental matrix.
His Algorithm study incorporates themes from Normalization, Noise and Estimator.
The various areas that he examines in his Ellipse study include Data point, Key, Reprojection error and Applied mathematics.
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.
