What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
Artificial intelligence, Computer vision, Digital watermarking, Watermark and Arousal are his primary areas of study.
Thierry Pun combines subjects such as Computer graphics and Pattern recognition with his study of Artificial intelligence.
In the subject of general Computer vision, his work in Data compression is often linked to Reed–Solomon error correction, thereby combining diverse domains of study.
His biological study spans a wide range of topics, including Computer security, Digital image, Robustness and Discrete cosine transform.
His research in Watermark intersects with topics in Data mining, Affine transformation, Human visual system model, Benchmark and Algorithm.
The concepts of his Arousal study are interwoven with issues in Valence, Speech recognition and Electroencephalography.
His most cited work include:
- DEAP: A Database for Emotion Analysis ;Using Physiological Signals (1495 citations)
- Rotation, scale and translation invariant spread spectrum digital image watermarking (712 citations)
- A Multimodal Database for Affect Recognition and Implicit Tagging (635 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Data mining, Digital watermarking and Algorithm.
His research on Artificial intelligence frequently links to adjacent areas such as Pattern recognition.
His work carried out in the field of Computer vision brings together such families of science as Mobility aid and Perception.
His research combines Information retrieval and Data mining.
He works mostly in the field of Digital watermarking, limiting it down to topics relating to Watermark and, in certain cases, Affine transformation, as a part of the same area of interest.
His Algorithm study incorporates themes from Encoder, Theoretical computer science, Additive white Gaussian noise and Robustness.
He most often published in these fields:
- Artificial intelligence (33.85%)
- Computer vision (22.56%)
- Data mining (15.38%)
What were the highlights of his more recent work (between 2007-2021)?
- Artificial intelligence (33.85%)
- Computer vision (22.56%)
- Affective computing (5.64%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Affective computing, Cognitive psychology and Perception are his primary areas of study.
His research investigates the link between Artificial intelligence and topics such as Machine learning that cross with problems in Subspace topology.
His Computer vision study focuses on Stereo camera in particular.
His Affective computing research is multidisciplinary, relying on both Valence, Arousal, Dynamic time warping, Affect and Multimedia.
His Valence study integrates concerns from other disciplines, such as Speech recognition, Eye tracking and Electroencephalography.
His studies deal with areas such as Feature extraction and Emotion classification as well as Electroencephalography.
Between 2007 and 2021, his most popular works were:
- DEAP: A Database for Emotion Analysis ;Using Physiological Signals (1495 citations)
- A Multimodal Database for Affect Recognition and Implicit Tagging (635 citations)
- Multimodal Emotion Recognition in Response to Videos (359 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
Thierry Pun mainly investigates Affective computing, Valence, Artificial intelligence, Arousal and Speech recognition.
His Affective computing research is multidisciplinary, incorporating perspectives in Social relation, Linkage, Index and Signal processing.
His Artificial intelligence research includes elements of Machine learning, Spatial correlation, Computer vision and Pattern recognition.
His Computer vision research integrates issues from Interface, Perception and PATH.
Thierry Pun has included themes like Cognitive psychology, Eye tracking, Affect and Search engine indexing in his Arousal study.
The Speech recognition study combines topics in areas such as Emotion assessment, Boredom and Electroencephalography.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
