D
Dorin Comaniciu
Researcher at Princeton University
Publications - 632
Citations - 43059
Dorin Comaniciu is an academic researcher from Princeton University. The author has contributed to research in topics: Segmentation & Object detection. The author has an hindex of 74, co-authored 622 publications receiving 40541 citations. Previous affiliations of Dorin Comaniciu include Siemens & Rutgers University.
Papers
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Journal Article
Component fusion for face detection in the presence of heteroscedastic noise
TL;DR: It is shown that this uncertainty carries important information that leads to increased face localization accuracy, and possible solutions taking into account geometrical constraints are discussed and compared.
Patent
Method and System for Model-Based Fusion of Multi-Modal Volumetric Images
Sasa Grbic,Razvan Ioan Ionasec,Yang Wang,Bogdan Georgescu,Tommaso Mansi,Dorin Comaniciu,Yefeng Zheng,Shaohua Kevin Zhou,Matthias John,Jan Dr. Boese +9 more
TL;DR: In this article, a method and system for fusion of multi-modal volumetric images is disclosed, where a model and a target anatomical structure and a transformation are jointly estimated from the first and second images.
Book ChapterDOI
Automatic localization of balloon markers and guidewire in rotational fluoroscopy with application to 3d stent reconstruction
TL;DR: An effective offline balloon marker tracking algorithm that leverages learning based detectors and employs the Viterbi algorithm to track the balloon markers in a globally optimal manner is presented, suggesting a great potential of the methods for clinical applications.
Patent
Method and system for up-vector detection for ribs in computed tomography volumes
David Liu,Hao Xu,Dijia Wu,Christian Tietjen,Grzegorz Soza,Shaohua Kevin Zhou,Dorin Comaniciu +6 more
TL;DR: In this article, an up-vector is automatically detected at each of a plurality of centerline points of the rib centerline of the at least one rib in a CT image.
Patent
Method and System for Catheter Tracking in Fluoroscopic Images Using Adaptive Discriminant Learning and Measurement Fusion
TL;DR: In this paper, an adaptive discriminant learning and measurement fusion for image-based catheter tracking is described, where the object may be tracked in the current frame based on a fusion of three types of measurement models including the adaptive discriminative model trained online, an object detection model trained offline, and an online appearance model.