What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Rachid Deriche mainly focuses on Artificial intelligence, Computer vision, Algorithm, Mathematical optimization and Image segmentation.
The various areas that Rachid Deriche examines in his Artificial intelligence study include Geodesic and Pattern recognition.
His Computer vision research integrates issues from Diffusion MRI, Position and Level set.
His Diffusion MRI study combines topics from a wide range of disciplines, such as Orientation, Sampling, Angular resolution and Design of experiments.
His Algorithm research includes elements of Smoothing, Real image, Partial differential equation and Image restoration.
His Mathematical optimization research is multidisciplinary, relying on both Kullback–Leibler divergence, Tensor, Regularization, Tikhonov regularization and Divergence.
His most cited work include:
- A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry (1294 citations)
- Using Canny's criteria to derive a recursively implemented optimal edge detector (978 citations)
- Geodesic active contours and level sets for the detection and tracking of moving objects (927 citations)
What are the main themes of his work throughout his whole career to date?
Rachid Deriche mostly deals with Artificial intelligence, Diffusion MRI, Algorithm, Computer vision and Pattern recognition.
His study in Machine learning extends to Artificial intelligence with its themes.
His biological study spans a wide range of topics, including Orientation, White matter, Mathematical analysis and Tensor.
The Algorithm study which covers Mathematical optimization that intersects with Basis.
His research is interdisciplinary, bridging the disciplines of Kalman filter and Computer vision.
His Pattern recognition research is multidisciplinary, incorporating elements of Inverse problem, Cortex, Cluster analysis and Human Connectome Project.
He most often published in these fields:
- Artificial intelligence (44.69%)
- Diffusion MRI (40.99%)
- Algorithm (28.15%)
What were the highlights of his more recent work (between 2017-2021)?
- Diffusion MRI (40.99%)
- Artificial intelligence (44.69%)
- Pattern recognition (17.53%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Diffusion MRI, Artificial intelligence, Pattern recognition, Algorithm and Tractography.
His study in Diffusion MRI is interdisciplinary in nature, drawing from both White matter, Brain tissue, Electroencephalography, Invariant and Spherical harmonics.
His Artificial intelligence study frequently links to related topics such as Signature.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Fiber, Eeg inverse problem, Image, Human Connectome Project and Brain activity and meditation.
In general Algorithm, his work in Regularization is often linked to Python linking many areas of study.
He combines subjects such as Fractional anisotropy, Voxel, Connectome and False positive paradox with his study of Tractography.
Between 2017 and 2021, his most popular works were:
- White matter information flow mapping from diffusion MRI and EEG. (9 citations)
- A unified framework for multimodal structure-function mapping based on eigenmodes. (8 citations)
- The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy. (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
Rachid Deriche mainly investigates Diffusion MRI, Algorithm, Synthetic data, Spherical harmonics and White matter.
In his work, Invariant and Orientation is strongly intertwined with Mathematical analysis, which is a subfield of Diffusion MRI.
His work on Regularization is typically connected to Rician fading as part of general Algorithm study, connecting several disciplines of science.
His Spherical harmonics research incorporates elements of Polarized light imaging, Optics and Unit sphere.
His White matter study combines topics in areas such as Microstructure imaging, Artificial intelligence and Pattern recognition.
His work on Segmentation as part of general Artificial intelligence study is frequently connected to Tumor growth, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
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