What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Geometry
Ronen Basri focuses on Artificial intelligence, Computer vision, Pattern recognition, Lambertian reflectance and Photometric stereo.
His Artificial intelligence study integrates concerns from other disciplines, such as Algorithm and Multigrid method.
His study in Cluster analysis extends to Computer vision with its themes.
Ronen Basri has researched Pattern recognition in several fields, including Rigid transformation, Visual recognition, Computation and Communication.
His Lambertian reflectance study combines topics in areas such as Image processing and Spherical harmonics.
His research in Photometric stereo intersects with topics in Basis, Active shape model, Face and Linear combination.
His most cited work include:
- Actions as space-time shapes (1531 citations)
- Lambertian reflectance and linear subspaces (1352 citations)
- Actions as Space-Time Shapes (1129 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Image.
His work on Artificial intelligence deals in particular with Segmentation, Object, Cognitive neuroscience of visual object recognition, Image segmentation and Pixel.
The various areas that Ronen Basri examines in his Computer vision study include Robot and Robustness.
His Pattern recognition research includes elements of Salient and Cluster analysis.
His work on Structure from motion as part of general Algorithm study is frequently connected to Set, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His studies in Photometric stereo integrate themes in fields like 3D reconstruction, Linear subspace and Face.
He most often published in these fields:
- Artificial intelligence (70.00%)
- Computer vision (50.56%)
- Pattern recognition (22.78%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (70.00%)
- Algorithm (21.67%)
- Artificial neural network (5.00%)
In recent papers he was focusing on the following fields of study:
Ronen Basri mostly deals with Artificial intelligence, Algorithm, Artificial neural network, Matrix and Set.
His work carried out in the field of Artificial intelligence brings together such families of science as Point, Computer vision and Pattern recognition.
When carried out as part of a general Computer vision research project, his work on Photometric stereo is frequently linked to work in Property, therefore connecting diverse disciplines of study.
His biological study spans a wide range of topics, including Image, Eigenvalues and eigenvectors and Robustness.
Ronen Basri combines subjects such as Function, Multigrid method, Solver and Linear system with his study of Artificial neural network.
Within one scientific family, Ronen Basri focuses on topics pertaining to Algebraic number under Matrix, and may sometimes address concerns connected to Fundamental matrix and Projective frame.
Between 2017 and 2021, his most popular works were:
- SpectralNet: Spectral Clustering using Deep Neural Networks (80 citations)
- SpectralNet: Spectral Clustering using Deep Neural Networks (41 citations)
- The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Geometry
His main research concerns Artificial intelligence, Algorithm, Algebraic number, Matrix and Deep learning.
The concepts of his Artificial intelligence study are interwoven with issues in Surface, Surface reconstruction, State and Computer vision.
His Algorithm research is multidisciplinary, incorporating elements of Matrix decomposition, Eigenvalues and eigenvectors and Training set.
The Algebraic number study combines topics in areas such as Projective frame, Euclidean geometry and Fundamental matrix.
His Matrix research integrates issues from Iterative reconstruction and Robustness.
In his research, VC dimension, Pattern recognition and Data point is intimately related to Cluster analysis, which falls under the overarching field of Deep learning.
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