What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Yuan Yuan mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Hyperspectral imaging.
His research on Artificial intelligence often connects related topics like Machine learning.
His studies deal with areas such as Feature, Object detection, Cluster analysis, Sparse matrix and Iterative reconstruction as well as Pattern recognition.
His work in Computer vision is not limited to one particular discipline; it also encompasses Convolutional neural network.
Yuan Yuan combines subjects such as Contextual image classification, Deep learning, Markov chain, Dimensionality reduction and Principal component analysis with his study of Feature extraction.
His work carried out in the field of Hyperspectral imaging brings together such families of science as Anomaly detection and Non-negative matrix factorization.
His most cited work include:
- Object tracking using SIFT features and mean shift (461 citations)
- Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking (297 citations)
- Segmentation of retinal blood vessels using the radial projection and semi-supervised approach (266 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Machine learning.
Hyperspectral imaging, Image, Feature, Pixel and Discriminative model are among the areas of Artificial intelligence where he concentrates his study.
His work deals with themes such as Subspace topology and Object detection, which intersect with Pattern recognition.
His Computer vision study frequently draws connections to adjacent fields such as Robustness.
His Feature extraction research is multidisciplinary, incorporating elements of Feature vector, Deep learning, Feature learning, Convolutional neural network and Remote sensing.
His work on Crowd counting as part of general Machine learning research is frequently linked to Field, thereby connecting diverse disciplines of science.
He most often published in these fields:
- Artificial intelligence (75.19%)
- Pattern recognition (44.56%)
- Computer vision (33.67%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (75.19%)
- Pattern recognition (44.56%)
- Feature extraction (18.73%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Feature extraction, Feature and Feature learning.
The Artificial intelligence study combines topics in areas such as Machine learning and Computer vision.
His work on Optical flow as part of general Computer vision study is frequently linked to Term, bridging the gap between disciplines.
His Pattern recognition research includes elements of RGB color model, Feature and Task.
His studies in Feature extraction integrate themes in fields like Convolution, Discriminative model, Convolutional neural network and Remote sensing.
In his research, Web page and Human–computer interaction is intimately related to Closed captioning, which falls under the overarching field of Feature.
Between 2019 and 2021, his most popular works were:
- Barrier Lyapunov Functions-Based Adaptive Fault Tolerant Control for Flexible Hypersonic Flight Vehicles With Full State Constraints (19 citations)
- Pixel-Wise Crowd Understanding via Synthetic Data (18 citations)
- Adaptive Fuzzy Tracking Control for Strict-Feedback Markov Jumping Nonlinear Systems With Actuator Failures and Unmodeled Dynamics (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of study are Artificial intelligence, Machine learning, Feature extraction, Pattern recognition and Pixel.
His study connects Focus and Artificial intelligence.
His work in the fields of Machine learning, such as Crowd counting, overlaps with other areas such as Source code and Domain.
His Feature extraction study is concerned with the larger field of Computer vision.
His work focuses on many connections between Pattern recognition and other disciplines, such as Task, that overlap with his field of interest in Feature and Embedding.
His research integrates issues of Segmentation, Hyperspectral imaging and Non-negative matrix factorization in his study of Pixel.
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