What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Internal medicine
Dorin Comaniciu mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Mean-shift.
His study in Artificial intelligence concentrates on Image segmentation, Image, Object detection, Discriminative model and Object.
The Computer vision study which covers Robustness that intersects with Convolutional neural network.
His study in the fields of Active shape model under the domain of Pattern recognition overlaps with other disciplines such as Initialization.
The concepts of his Segmentation study are interwoven with issues in False positive paradox, Database, Classifier, Cluster analysis and Radiology.
His Mean-shift research also works with subjects such as
- Kalman filter, Similarity measure and Kernel most often made with reference to Bhattacharyya distance,
- Algorithm which is related to area like Particle filter and Kernel.
His most cited work include:
- Mean shift: a robust approach toward feature space analysis (9295 citations)
- Kernel-based object tracking (4322 citations)
- Real-time tracking of non-rigid objects using mean shift (2814 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Object detection, Discriminative model, Classifier, Object and Robustness.
His research in Computer vision intersects with topics in Fluoroscopy, Fluoroscopic image, Ultrasound and Medical imaging.
His studies in Pattern recognition integrate themes in fields like Boosting, Deep learning and Feature.
Much of his study explores Boosting relationship to Probabilistic logic.
The study incorporates disciplines such as Voxel and Radiology in addition to Segmentation.
He most often published in these fields:
- Artificial intelligence (65.26%)
- Computer vision (45.38%)
- Pattern recognition (26.45%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (65.26%)
- Pattern recognition (26.45%)
- Deep learning (4.38%)
In recent papers he was focusing on the following fields of study:
Dorin Comaniciu focuses on Artificial intelligence, Pattern recognition, Deep learning, Computer vision and Image.
His work on Artificial intelligence is being expanded to include thematically relevant topics such as Machine learning.
His Pattern recognition research integrates issues from Image processing, Radiography, Nodule and Robustness.
Dorin Comaniciu has researched Deep learning in several fields, including Class, Feature extraction, Active shape model and Prostate cancer.
The Image registration, Object and Landmark research Dorin Comaniciu does as part of his general Computer vision study is frequently linked to other disciplines of science, such as Process, therefore creating a link between diverse domains of science.
Dorin Comaniciu works mostly in the field of Image, limiting it down to topics relating to Domain and, in certain cases, Surface geometry, as a part of the same area of interest.
Between 2015 and 2021, his most popular works were:
- A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. (153 citations)
- Automatic Liver Segmentation Using Adversarial Image-to-Image Network (114 citations)
- Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans (105 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Computer vision, Image, Deep learning and Pattern recognition.
As part of his studies on Artificial intelligence, Dorin Comaniciu often connects relevant areas like Machine learning.
His Landmark, Image registration and Ground truth study in the realm of Computer vision connects with subjects such as Sample point.
His Image research includes themes of Parsing and Computed tomography.
His biological study spans a wide range of topics, including Feature extraction, Image segmentation and Benchmark.
His research in the fields of Classifier and Feature vector overlaps with other disciplines such as Endoscopic image.
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.
