2019 - Member of the National Academy of Medicine (NAM)
2017 - ACM Fellow For contributions to machine intelligence, diagnostic imaging, image-guided interventions, and computer vision
2013 - Fellow of the Indian National Academy of Engineering (INAE)
2012 - IEEE Fellow For contributions to medical image analysis and computer vision
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
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.
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.
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.
Mean shift: a robust approach toward feature space analysis
D. Comaniciu;P. Meer.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Kernel-based object tracking
D. Comaniciu;V. Ramesh;P. Meer.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2003)
Real-time tracking of non-rigid objects using mean shift
D. Comaniciu;V. Ramesh;P. Meer.
computer vision and pattern recognition (2000)
Mean shift analysis and applications
D. Comaniciu;P. Meer.
international conference on computer vision (1999)
Robust analysis of feature spaces: color image segmentation
D. Comaniciu;P. Meer.
computer vision and pattern recognition (1997)
Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features
Yefeng Zheng;A. Barbu;B. Georgescu;M. Scheuering.
IEEE Transactions on Medical Imaging (2008)
The variable bandwidth mean shift and data-driven scale selection
D. Comaniciu;V. Ramesh;P. Meer.
international conference on computer vision (2001)
Total variation models for variable lighting face recognition
T. Chen;Wotao Yin;Xiang Sean Zhou;D. Comaniciu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)
An algorithm for data-driven bandwidth selection
D. Comaniciu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2003)
Mean shift and optimal prediction for efficient object tracking
D. Comaniciu;V. Ramesh.
international conference on image processing (2000)
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