What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Image restoration, Algorithm and Image processing.
His research integrates issues of Parameter identification problem, Estimation theory, Masking and Pattern recognition in his study of Artificial intelligence.
In general Computer vision study, his work on Filter, Orientation, Channel and Data compression often relates to the realm of Visual perception, thereby connecting several areas of interest.
His Image restoration study combines topics from a wide range of disciplines, such as Signal-to-noise ratio, Iterative method and Circulant matrix.
The Algorithm study combines topics in areas such as Kalman filter and Sub-band coding.
His Image processing study which covers Iterative reconstruction that intersects with Pixel, Inpainting, Edge detection, Interpolation and Feature extraction.
His most cited work include:
- Iterative methods for image deblurring (413 citations)
- Regularized iterative image restoration with ringing reduction (275 citations)
- Automated high-level movie segmentation for advanced video-retrieval systems (269 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Algorithm, Image restoration and Image.
His research in Artificial intelligence intersects with topics in Filter and Pattern recognition.
His Algorithm study integrates concerns from other disciplines, such as Mathematical optimization, Speech recognition, Image sequence and Sub-band coding.
Jan Biemond combines subjects such as Digital television and Harmonic Vector Excitation Coding with his study of Sub-band coding.
In Image restoration, Jan Biemond works on issues like Estimation theory, which are connected to Expectation–maximization algorithm.
Jan Biemond has researched Image processing in several fields, including Noise and Visual communication.
He most often published in these fields:
- Artificial intelligence (58.22%)
- Computer vision (51.37%)
- Algorithm (28.77%)
What were the highlights of his more recent work (between 1999-2016)?
- Artificial intelligence (58.22%)
- Computer vision (51.37%)
- Image restoration (25.34%)
In recent papers he was focusing on the following fields of study:
Jan Biemond focuses on Artificial intelligence, Computer vision, Image restoration, Image processing and Image.
He mostly deals with Robustness in his studies of Artificial intelligence.
His research ties Algorithm and Computer vision together.
His Image restoration research integrates issues from Image sequence, Feature detection, Image texture, Pattern recognition and Digital recording.
When carried out as part of a general Pattern recognition research project, his work on Image segmentation is frequently linked to work in Model parameters, therefore connecting diverse disciplines of study.
Jan Biemond interconnects Video capture, Discrete cosine transform, Deinterlacing, Data compression and Feature extraction in the investigation of issues within Image processing.
Between 1999 and 2016, his most popular works were:
- Basic Methods for Image Restoration and Identification (96 citations)
- Edge-based image restoration (67 citations)
- Image and Video Databases: Restoration, Watermarking and Retrieval (39 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
Jan Biemond spends much of his time researching Artificial intelligence, Computer vision, Image processing, Image restoration and Motion estimation.
His Artificial intelligence study frequently draws connections between related disciplines such as Reduction.
His work on Data compression, Image compression, Feature detection and Point spread function is typically connected to Coring as part of general Computer vision study, connecting several disciplines of science.
His work in the fields of Image processing, such as Digital image, overlaps with other areas such as Quality.
His work deals with themes such as Real image, Filter, Motion blur and Digital recording, which intersect with Image restoration.
His Motion estimation study deals with Motion compensation intersecting with Motion analysis, Image segmentation, Pattern recognition and Contextual image classification.
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