What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of investigation include Artificial intelligence, Computer vision, Radar imaging, Synthetic aperture radar and Image formation.
Mujdat Cetin performs multidisciplinary studies into Artificial intelligence and Accelerometer in his work.
Mujdat Cetin has researched Computer vision in several fields, including Yawn and Robustness.
His study in Radar imaging is interdisciplinary in nature, drawing from both Algorithm, Compressed sensing, Inverse problem and Iterative reconstruction.
The concepts of his Algorithm study are interwoven with issues in Image restoration and Signal reconstruction.
His Image formation research focuses on Sparse approximation and how it connects with Iterative method, Mathematical optimization and Basis pursuit.
His most cited work include:
- A sparse signal reconstruction perspective for source localization with sensor arrays (1755 citations)
- Sparsity and Compressed Sensing in Radar Imaging (527 citations)
- Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization (445 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Computer vision, Synthetic aperture radar, Pattern recognition and Algorithm.
His Artificial intelligence and Segmentation, Iterative reconstruction, Image segmentation, Scale-space segmentation and Sparse approximation investigations all form part of his Artificial intelligence research activities.
His studies in Computer vision integrate themes in fields like Inverse synthetic aperture radar and Phase.
The various areas that Mujdat Cetin examines in his Synthetic aperture radar study include Regularization, Clutter, Radar imaging and Image formation.
His study looks at the intersection of Pattern recognition and topics like Electroencephalography with Classifier.
His work on Optimization problem as part of general Algorithm study is frequently linked to Initialization, therefore connecting diverse disciplines of science.
He most often published in these fields:
- Artificial intelligence (71.02%)
- Computer vision (47.13%)
- Synthetic aperture radar (33.12%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (71.02%)
- Pattern recognition (27.71%)
- Synthetic aperture radar (33.12%)
In recent papers he was focusing on the following fields of study:
Mujdat Cetin mainly investigates Artificial intelligence, Pattern recognition, Synthetic aperture radar, Algorithm and Computer vision.
His Pattern recognition research incorporates elements of Density estimation, Computation, Feature and Electroencephalography.
His work deals with themes such as Clutter, Image formation, Iterative reconstruction and Radar imaging, which intersect with Synthetic aperture radar.
In the subject of general Algorithm, his work in Optimization problem is often linked to Simulated data, thereby combining diverse domains of study.
His work carried out in the field of Computer vision brings together such families of science as Visualization, Phase, Sparse approximation and Hybrid Monte Carlo.
His Sparse approximation research integrates issues from Background image, Signal and Component.
Between 2016 and 2021, his most popular works were:
- Recursive Bayesian Coding for BCIs (19 citations)
- Dendritic spine classification using shape and appearance features based on two-photon microscopy. (18 citations)
- Nonparametric Joint Shape and Feature Priors for Image Segmentation (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of study are Artificial intelligence, Pattern recognition, Synthetic aperture radar, Algorithm and Computer vision.
As part of one scientific family, Mujdat Cetin deals mainly with the area of Pattern recognition, narrowing it down to issues related to the Feature, and often Active contour model, Image processing and Support vector machine.
In his research on the topic of Synthetic aperture radar, Automatic target recognition is strongly related with Image formation.
His Algorithm study combines topics from a wide range of disciplines, such as Mathematical optimization, Prior probability and Inverse problem.
His Inverse problem research includes themes of Regularization, Optimization problem, Noise measurement and Robustness.
His Computer vision study incorporates themes from Inverse synthetic aperture radar, Phase and Sparse approximation.
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.
