What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Motion estimation.
Feature extraction, Hidden Markov model, Segmentation, Boosting and Iterative reconstruction are the subjects of his Artificial intelligence studies.
His Computer vision research focuses on subjects like Motion, which are linked to Ventricle and Biomedical engineering.
His work deals with themes such as Adversarial system, Facial recognition system, Generative grammar and Feature, which intersect with Pattern recognition.
His Machine learning study incorporates themes from Statistical learning, Theoretical computer science, Bayesian probability and Greedy algorithm.
His research integrates issues of Cognitive neuroscience of visual object recognition and Generalized forces in his study of Motion estimation.
His most cited work include:
- A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI (935 citations)
- Self-Attention Generative Adversarial Networks (890 citations)
- Dynamic 3D models with local and global deformations: deformable superquadrics (760 citations)
What are the main themes of his work throughout his whole career to date?
Dimitris N. Metaxas mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image segmentation.
His study brings together the fields of Machine learning and Artificial intelligence.
His research related to Tracking, Motion estimation, Face, Motion and Iterative reconstruction might be considered part of Computer vision.
His Pattern recognition research is multidisciplinary, relying on both Contextual image classification, Facial recognition system and Image retrieval.
His research investigates the connection between Facial recognition system and topics such as Facial expression that intersect with issues in Speech recognition.
His research in Segmentation intersects with topics in Magnetic resonance imaging, Initialization, Object and Boundary.
He most often published in these fields:
- Artificial intelligence (69.15%)
- Computer vision (38.43%)
- Pattern recognition (28.42%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (69.15%)
- Pattern recognition (28.42%)
- Segmentation (21.24%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Deep learning.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Code.
His Pattern recognition research includes themes of Object detection, Minimum bounding box, Feature and Cluster analysis.
His Segmentation study deals with Magnetic resonance imaging intersecting with Medical imaging.
His Computer vision study combines topics from a wide range of disciplines, such as Frame and Trajectory.
His research in Deep learning tackles topics such as Discriminator which are related to areas like Real image.
Between 2018 and 2021, his most popular works were:
- StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks (339 citations)
- Weakly Supervised Deep Nuclei Segmentation using Points Annotation in Histopathology Images (25 citations)
- Improving Nuclei/Gland Instance Segmentation in Histopathology Images by Full Resolution Neural Network and Spatial Constrained Loss (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Dimitris N. Metaxas mainly focuses on Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Deep learning.
His biological study deals with issues like Code, which deal with fields such as Detector.
Dimitris N. Metaxas interconnects Leverage and Gesture recognition in the investigation of issues within Pattern recognition.
His Segmentation research incorporates themes from Idiopathic scoliosis, Cobb angle and Vertebra.
His work on Motion, Iterative reconstruction, Landmark and Feature extraction as part of general Computer vision research is frequently linked to Articular cartilage, bridging the gap between disciplines.
His biological study spans a wide range of topics, including Positive-definite matrix, Theoretical computer science, Contextual image classification, Joint and Compressed sensing.
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