What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Artificial intelligence
- Computer vision
Jean-François Aujol focuses on Image processing, Image restoration, Mathematical analysis, Algorithm and Mathematical optimization.
Image processing is a subfield of Artificial intelligence that he tackles.
As a part of the same scientific study, Jean-François Aujol usually deals with the Image restoration, concentrating on Dykstra's projection algorithm and frequently concerns with Topology, Variable and Bounded variation.
His studies examine the connections between Mathematical analysis and genetics, as well as such issues in Image, with regards to Calculus of variations and Uniqueness.
His studies in Algorithm integrate themes in fields like Norm and Hilbert space.
His Mathematical optimization research incorporates elements of Efficient algorithm and Color normalization.
His most cited work include:
- Structure-Texture Image Decomposition--Modeling, Algorithms, and Parameter Selection (510 citations)
- A Variational Approach to Removing Multiplicative Noise (383 citations)
- Dual Norms and Image Decomposition Models (288 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Computer vision, Algorithm, Image and Image processing.
His work in Artificial intelligence addresses issues such as Pattern recognition, which are connected to fields such as Noise reduction.
As a member of one scientific family, Jean-François Aujol mostly works in the field of Computer vision, focusing on Lidar and, on occasion, Point.
The study incorporates disciplines such as Photometric stereo, Boundary, Image restoration and Texture synthesis in addition to Algorithm.
Jean-François Aujol has included themes like Simple, Luminance, Mathematical analysis and Topographic map in his Image study.
His Image processing study integrates concerns from other disciplines, such as Norm, Feature extraction and Mathematical optimization.
He most often published in these fields:
- Artificial intelligence (50.00%)
- Computer vision (40.44%)
- Algorithm (39.71%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (50.00%)
- Computer vision (40.44%)
- Algorithm (39.71%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Computer vision, Algorithm, Applied mathematics and Image.
His study in the fields of Image colorization and Variety under the domain of Artificial intelligence overlaps with other disciplines such as Prior probability, Relation and Focus.
In his study, which falls under the umbrella issue of Computer vision, Point cloud and Point is strongly linked to Lidar.
His Algorithm research is multidisciplinary, incorporating perspectives in Principal component analysis and Fourier transform.
Jean-François Aujol combines subjects such as Image resolution, Machine learning, Multispectral image and Pattern recognition with his study of Image.
His Segmentation study combines topics from a wide range of disciplines, such as Margin and Image processing.
Between 2018 and 2021, his most popular works were:
- Convergence rate of inertial Forward–Backward algorithm beyond Nesterov’s rule (26 citations)
- Rayleigh quotient minimization for absolutely one-homogeneous functionals (15 citations)
- The basins of attraction of the global minimizers of the non-convex sparse spike estimation problem (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Mathematical analysis
Jean-François Aujol spends much of his time researching Applied mathematics, Computer vision, Artificial intelligence, Segmentation and Minification.
His work on Ode as part of general Applied mathematics study is frequently linked to Convex optimization, Rate of convergence and Flatness, therefore connecting diverse disciplines of science.
His Computer vision research incorporates themes from Margin and Lidar.
His research integrates issues of Descent, Heuristics and Greedy algorithm in his study of Minification.
His Point cloud research includes themes of Image, Task and Feature extraction.
His studies deal with areas such as End-to-end principle, Point and Image segmentation as well as Image processing.
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