What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Tsuhan Chen spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Facial recognition system.
His Machine learning research extends to the thematically linked field of Artificial intelligence.
His work in Computer vision tackles topics such as Computer graphics which are related to areas like Image coding, Stereopsis and Application software.
His work carried out in the field of Pattern recognition brings together such families of science as Cognitive neuroscience of visual object recognition, Feature, Artificial neural network, Representation and Probabilistic logic.
His Feature extraction research includes elements of Contrast, Image, Closed captioning, Speech recognition and Visualization.
His Facial recognition system research is multidisciplinary, incorporating elements of Context, Multimedia and 3D single-object recognition.
His most cited work include:
- Recent advances in convolutional neural networks (1099 citations)
- iCoseg: Interactive co-segmentation with intelligent scribble guidance (410 citations)
- Understanding images of groups of people (291 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Machine learning are his primary areas of study.
Artificial intelligence is represented through his Image segmentation, Facial recognition system, Object detection, Object and Segmentation research.
His Facial recognition system study is concerned with Face in general.
Tsuhan Chen frequently studies issues relating to Categorization and Object detection.
While the research belongs to areas of Computer vision, Tsuhan Chen spends his time largely on the problem of Computer graphics, intersecting his research to questions surrounding Iterative reconstruction.
As part of the same scientific family, Tsuhan Chen usually focuses on Pattern recognition, concentrating on Image retrieval and intersecting with Information retrieval.
He most often published in these fields:
- Artificial intelligence (70.20%)
- Computer vision (49.49%)
- Pattern recognition (28.28%)
What were the highlights of his more recent work (between 2012-2018)?
- Artificial intelligence (70.20%)
- Computer vision (49.49%)
- Image (8.84%)
In recent papers he was focusing on the following fields of study:
Tsuhan Chen mainly focuses on Artificial intelligence, Computer vision, Image, Pattern recognition and Information retrieval.
His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning.
His Computer vision research focuses on Robot and how it relates to Index finger, Point and Calibration.
His Image research incorporates themes from Speech recognition, Inference and Transfer.
His biological study spans a wide range of topics, including Blood flow, Parametric statistics, Face detection, Cluster analysis and Functional imaging.
He has researched Information retrieval in several fields, including Data mining, Focus and Personalization.
Between 2012 and 2018, his most popular works were:
- Recent advances in convolutional neural networks (1099 citations)
- A mixed bag of emotions: Model, predict, and transfer emotion distributions (110 citations)
- 3D-Based Reasoning with Blocks, Support, and Stability (105 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His scientific interests lie mostly in Artificial intelligence, Computer vision, Convolutional neural network, Pattern recognition and Machine learning.
Tsuhan Chen is interested in Feature extraction, which is a branch of Artificial intelligence.
His Computer vision study incorporates themes from Sparse approximation, Global Positioning System, Functional imaging and Perfusion.
The concepts of his Convolutional neural network study are interwoven with issues in Artificial neural network and Deep learning.
His work on Segmentation and Image segmentation as part of general Pattern recognition study is frequently linked to Layer, bridging the gap between disciplines.
His Machine learning study integrates concerns from other disciplines, such as Contextual image classification and Facial recognition system.
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