His main research concerns Artificial intelligence, Computer vision, Augmented reality, Segmentation and Pose. His research brings together the fields of Pattern recognition and Artificial intelligence. His Computer vision research includes themes of Computer graphics and Ultrasound.
His Augmented reality study also includes fields such as
Nassir Navab mainly focuses on Artificial intelligence, Computer vision, Augmented reality, Pattern recognition and Segmentation. Many of his studies on Artificial intelligence apply to Machine learning as well. His studies deal with areas such as Imaging phantom, Ultrasound, Computer graphics and Robustness as well as Computer vision.
His Augmented reality study necessitates a more in-depth grasp of Human–computer interaction. His research is interdisciplinary, bridging the disciplines of Feature and Pattern recognition. Specifically, his work in Segmentation is concerned with the study of Image segmentation.
His primary areas of investigation include Artificial intelligence, Computer vision, Machine learning, Segmentation and Pattern recognition. His study in Deep learning, Convolutional neural network, Artificial neural network, Augmented reality and Medical imaging is carried out as part of his studies in Artificial intelligence. His Computer vision research includes elements of Imaging phantom and Visualization.
In his work, Graph is strongly intertwined with Graph, which is a subfield of Machine learning. A large part of his Segmentation studies is devoted to Image segmentation. Nassir Navab has included themes like Ground truth and Robustness in his Pattern recognition study.
Nassir Navab focuses on Artificial intelligence, Computer vision, Deep learning, Segmentation and Pattern recognition. He combines subjects such as Machine learning and Graph with his study of Artificial intelligence. The various areas that he examines in his Computer vision study include Visualization and Benchmark.
The concepts of his Deep learning study are interwoven with issues in Embedding and Data mining. His biological study spans a wide range of topics, including Pixel and Medical imaging. In general Pattern recognition, his work in Training set is often linked to Transfer linking many areas of study.
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.
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Fausto Milletari;Nassir Navab;Seyed-Ahmad Ahmadi.
international conference on 3d vision (2016)
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.
Babak Ehteshami Bejnordi;Mitko Veta;Paul Johannes van Diest;Bram van Ginneken.
Deeper Depth Prediction with Fully Convolutional Residual Networks
Iro Laina;Christian Rupprecht;Vasileios Belagiannis;Federico Tombari.
international conference on 3d vision (2016)
Tissue Classification as a Potential Approach for Attenuation Correction in Whole-Body PET/MRI: Evaluation with PET/CT Data
Axel Martinez-Möller;Michael Souvatzoglou;Gaspar Delso;Ralph A. Bundschuh.
The Journal of Nuclear Medicine (2009)
Model globally, match locally: Efficient and robust 3D object recognition
Bertram Drost;Markus Ulrich;Nassir Navab;Slobodan Ilic.
computer vision and pattern recognition (2010)
Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes
Stefan Hinterstoisser;Vincent Lepetit;Slobodan Ilic;Stefan Holzer.
asian conference on computer vision (2012)
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010
Tianzi Jiang;Nassir Navab;Josien P. W. Pluim;Max A. Viergever.
Lecture Notes in Computer Science (2010)
Dense image registration through MRFs and efficient linear programming.
Ben Glocker;Ben Glocker;Nikos Komodakis;Nikos Komodakis;Georgios Tziritas;Nassir Navab.
Medical Image Analysis (2008)
Gradient Response Maps for Real-Time Detection of Textureless Objects
S. Hinterstoisser;C. Cagniart;S. Ilic;P. Sturm.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge
K. Murphy;B. van Ginneken;J. M. Reinhardt;S. Kabus.
IEEE Transactions on Medical Imaging (2011)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: