What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Artificial intelligence
- Algorithm
Tony F. Chan focuses on Algorithm, Artificial intelligence, Computer vision, Image restoration and Image processing.
His Algorithm research integrates issues from Active contour model, Mathematical optimization, Minification, Image and Wavelet.
His research integrates issues of Mumford–Shah functional, Level set and Object detection in his study of Active contour model.
His research in the fields of Segmentation and Digital image overlaps with other disciplines such as Low-pass filter, Digital filter and Video denoising.
His Image restoration research incorporates elements of Linearization, Blind deconvolution, Line search, Total variation denoising and Rate of convergence.
Tony F. Chan combines subjects such as Norm, Image segmentation and Regular polygon with his study of Image processing.
His most cited work include:
- Active contours without edges (8346 citations)
- A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model (2152 citations)
- Weighted essentially non-oscillatory schemes (2098 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Algorithm, Artificial intelligence, Mathematical analysis, Computer vision and Applied mathematics.
His research investigates the connection between Algorithm and topics such as Mathematical optimization that intersect with issues in Multigrid method.
His study in Segmentation, Image, Image segmentation and Inpainting is done as part of Artificial intelligence.
His Mathematical analysis study incorporates themes from Surface, Conjugate gradient method and Nonlinear system.
Tony F. Chan is interested in Image processing, which is a branch of Computer vision.
The various areas that Tony F. Chan examines in his Applied mathematics study include Matrix and Domain decomposition methods.
He most often published in these fields:
- Algorithm (26.65%)
- Artificial intelligence (22.39%)
- Mathematical analysis (18.12%)
What were the highlights of his more recent work (between 2007-2020)?
- Algorithm (26.65%)
- Artificial intelligence (22.39%)
- Computer vision (17.70%)
In recent papers he was focusing on the following fields of study:
Tony F. Chan mainly focuses on Algorithm, Artificial intelligence, Computer vision, Mathematical analysis and Surface.
His work carried out in the field of Algorithm brings together such families of science as Image restoration, Mathematical optimization and Topology.
His study in Artificial intelligence concentrates on Image segmentation, Segmentation, Inpainting and Image texture.
His Image segmentation research is multidisciplinary, incorporating elements of Histogram and Piecewise.
His research in Image processing and Image are components of Computer vision.
His studies deal with areas such as Numerical analysis and Wavelet as well as Image processing.
Between 2007 and 2020, his most popular works were:
- A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science (492 citations)
- Fast dual minimization of the vectorial total variation norm and applications to color image processing (303 citations)
- Make it home: automatic optimization of furniture arrangement (243 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Artificial intelligence
- Geometry
The scientist’s investigation covers issues in Algorithm, Artificial intelligence, Computer vision, Image processing and Image restoration.
The study incorporates disciplines such as Active contour model and Mathematical optimization, Minification in addition to Algorithm.
His study on Skin color is often connected to Casual as part of broader study in Computer vision.
His work deals with themes such as Initial value problem and Image segmentation, which intersect with Image processing.
Many of his research projects under Image segmentation are closely connected to Relaxation technique with Relaxation technique, tying the diverse disciplines of science together.
His Augmented Lagrangian method research also works with subjects such as
- Segmentation, which have a strong connection to Topology,
- Numerical analysis, which have a strong connection to Mean curvature.
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