What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
The scientist’s investigation covers issues in Digital watermarking, Artificial intelligence, Computer vision, Multimedia and Image processing.
His Digital watermarking study incorporates themes from Information hiding, Embedding, Fingerprint recognition, Color image and Robustness.
Min Wu interconnects Binary image, Data mining and Theoretical computer science in the investigation of issues within Information hiding.
In general Artificial intelligence study, his work on Wavelet transform, Image sensor, Data compression and Transform coding often relates to the realm of Phase correlation, thereby connecting several areas of interest.
His studies in Computer vision integrate themes in fields like Spectral density estimation and Multidimensional signal processing.
The study incorporates disciplines such as Keying and Cryptography in addition to Multimedia.
His most cited work include:
- Rotation, scale, and translation resilient watermarking for images (619 citations)
- Robust and secure image hashing (466 citations)
- Anti-collusion fingerprinting for multimedia (350 citations)
What are the main themes of his work throughout his whole career to date?
Min Wu mostly deals with Artificial intelligence, Computer vision, Digital watermarking, Multimedia and Embedding.
His research investigates the connection between Artificial intelligence and topics such as Signal that intersect with issues in Real-time computing.
His studies link Interpolation with Computer vision.
Min Wu has included themes like Watermark, Theoretical computer science, Computer security, Algorithm and Robustness in his Digital watermarking study.
Min Wu combines Multimedia and Collusion in his studies.
The Information hiding study combines topics in areas such as Binary image and Authentication.
He most often published in these fields:
- Artificial intelligence (36.57%)
- Computer vision (24.63%)
- Digital watermarking (17.54%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (36.57%)
- Computer vision (24.63%)
- Pattern recognition (10.82%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Pattern recognition, Real-time computing and Photoplethysmogram are his primary areas of study.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Signal.
His work in Computer vision is not limited to one particular discipline; it also encompasses Field.
His work in Pattern recognition addresses issues such as Waveform, which are connected to fields such as Discrete cosine transform and Preprocessor.
His Real-time computing research is multidisciplinary, relying on both Wireless, Channel state information, Authentication and Home automation.
In most of his Steganography studies, his work intersects topics such as Multimedia.
Between 2016 and 2021, his most popular works were:
- SMARS: Sleep Monitoring via Ambient Radio Signals (20 citations)
- Counterfeit Detection Based on Unclonable Feature of Paper Using Mobile Camera (13 citations)
- Factors Affecting ENF Capture in Audio (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
His scientific interests lie mostly in Artificial intelligence, Computer vision, Real-time computing, Wireless and Transmitter.
His biological study spans a wide range of topics, including Photoplethysmogram and Flicker.
Min Wu combines subjects such as Waveform, Pattern recognition and Discrete cosine transform with his study of Photoplethysmogram.
His study ties his expertise on Computer graphics together with the subject of Computer vision.
His studies deal with areas such as Radio propagation, Wireless sensor network and Robustness as well as Wireless.
His Transmitter research is multidisciplinary, incorporating perspectives in Communication channel, Multipath channels and Wireless signal.
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.
