What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Wavelet transform.
His Computer vision study combines topics from a wide range of disciplines, such as Salient and Process.
He has included themes like Kullback–Leibler divergence, Inpainting, Image, Inference and Generative model in his Pattern recognition study.
His work on Computational complexity theory is typically connected to Cartesian tensor as part of general Algorithm study, connecting several disciplines of science.
His Wavelet transform study deals with the bigger picture of Wavelet.
His Contourlet research incorporates elements of Filter bank and Curvelet.
His most cited work include:
- The contourlet transform: an efficient directional multiresolution image representation (3282 citations)
- The Nonsubsampled Contourlet Transform: Theory, Design, and Applications (1545 citations)
- Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance (1065 citations)
What are the main themes of his work throughout his whole career to date?
Minh N. Do mostly deals with Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Wavelet.
His research in Image processing, Pixel, Wavelet transform, Image and Contourlet are components of Artificial intelligence.
Many of his studies involve connections with topics such as Filter and Contourlet.
His Computer vision study often links to related topics such as Computer graphics.
His Algorithm research focuses on Filter bank and how it relates to Filter design.
His work is dedicated to discovering how Wavelet, Image retrieval are connected with Image texture and other disciplines.
He most often published in these fields:
- Artificial intelligence (59.02%)
- Computer vision (40.06%)
- Algorithm (24.77%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (59.02%)
- Computer vision (40.06%)
- Pattern recognition (16.82%)
In recent papers he was focusing on the following fields of study:
Minh N. Do focuses on Artificial intelligence, Computer vision, Pattern recognition, Feature and Machine learning.
All of his Artificial intelligence and Deep learning, Object detection, Optical flow, Convolution and Minimum bounding box investigations are sub-components of the entire Artificial intelligence study.
His Convolution study also includes
- Component, which have a strong connection to Constraint, Focus, Feature vector and Pixel,
- Artificial neural network that intertwine with fields like Elastic net regularization, Radiology and Modality.
His work on RGB color model, Object and Image based as part of general Computer vision study is frequently linked to CAD and Track, therefore connecting diverse disciplines of science.
Convolutional neural network is the focus of his Pattern recognition research.
His work in Feature tackles topics such as Image stitching which are related to areas like Trajectory, Homography and Position.
Between 2016 and 2021, his most popular works were:
- Semantic Image Inpainting with Deep Generative Models (665 citations)
- Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train (134 citations)
- Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning. (54 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
Minh N. Do mainly focuses on Artificial intelligence, Computer vision, Deep learning, Machine learning and Algorithm.
His research on Artificial intelligence frequently links to adjacent areas such as Pattern recognition.
The various areas that he examines in his Pattern recognition study include Similarity and Pattern matching.
In general Computer vision, his work in Video processing, Shape analysis and Geometric primitive is often linked to Geometry processing linking many areas of study.
In his study, Random forest and Test set is strongly linked to Image segmentation, which falls under the umbrella field of Machine learning.
His research investigates the connection between Algorithm and topics such as Tensor that intersect with issues in Dimension, Computation, Compression and Matrix decomposition.
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