I am a postdoctoral research associate at the University of Maryland, associated with the Computer Vision Lab and the Institute for Advanced Computer Studies. I received my doctoral degree in Computer Science at the University of Maryland, College Park, with Prof. Larry Davis as my advisor. I completed my B.S. and M.S. degrees at Penn State University (with Prof. Octavia Camps as my research advisor). My research interests are in Computer Vision, Pattern Recognition, Machine Learning, and Artificial Intelligence.
Hyungtae Lee, Vlad I. Morariu, Larry S. Davis. Clauselets: Leveraging Temporally Related Actions for Video Event Analysis. IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. [to appear] BibTeX
Yen-Liang Lin, Vlad I. Morariu, Winston Hsu, Larry S. Davis. Jointly Optimizing 3D Model Fitting and Fine-Grained Classification. European Conference on Computer Vision (ECCV), 2014. PDF BibTeX Dataset
Varun K. Nagaraja, Vlad I. Morariu, Larry S. Davis. Feedback Loop between High Level Semantics and Low Level Vision. European Conference on Computer Vision Workshop (ECCVW), 2014. PDF Supplementary BibTeX
Send me your email, and I will let you know when the code and dataset are available.
Radu Dondera, Vlad I. Morariu, Yulu Wang, and Larry S. Davis. Interactive Video Segmentation Using Occlusion Boundaries and Temporally Coherent Superpixels. IEEE Winter Conference on Applications of Computer Vision (WACV), 2014. PDF BibTeX
Radu Dondera, Vlad I. Morariu, Larry S. Davis. Learning to Detect Carried Objects with Minimal Supervision. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2013. PDF BibTeX
Vlad I. Morariu, David Harwood, and Larry S. Davis. Tracking People's Hands and Feet Using Mixed Network AND/OR Search. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2013. PDF BibTeX coming soon: [software] [videos]
Fatemeh Mirrashed, Vlad I. Morariu, Behjat Siddiquie, Rogerio S. Feris, and Larry S. Davis. Domain Adaptive Object Detection. IEEE Workshop on the Applications of Computer Vision (WACV), 2013. PDF BibTeX
Sameh Khamis, Vlad I. Morariu, and Larry S. Davis. A Flow Model for Joint Action Recognition and Identity Maintenance. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. PDF BibTeX Errata
Ryan Farrell, Om Oza, Ning Zhang, Vlad I. Morariu, Trevor Darrell, and Larry S. Davis. Subordinate Categorization Using Volumetric Primitives and Pose-Normalized Appearance. IEEE International Conference on Computer Vision (ICCV), 2011. [ORAL] PDF BibTeX
Vlad I. Morariu, Balaji Vasan Srinivasan, Vikas C. Raykar, Ramani Duraiswami, and Larry S. Davis. Automatic online tuning for fast Gaussian summation. Advances in Neural Information Processing Systems (NIPS), 2008. PDF BibTeX
Vlad I. Morariu, V. Shiv Naga Prasad, and Larry S. Davis. Human Activity Understanding using Visibility Context. IEEE/RSJ IROS Workshop: From sensors to human spatial concepts (FS2HSC), 2007. PDF BibTeX
Benjamin Fransen, Vlad Morariu, Eric Martinson, Samuel Blisard, Matthew Marge, Scott Thomas, Alan Schultz, and Dennis Perzanowski. Using Vision, Acoustics, and Natural Language for Disambiguation. IEEE International Conference on Human-Robot Interaction (HRI) 2007. PDF BibTeX
Vlad I. Morariu, Octavia I. Camps, Mario Sznaier, and Hwasup Lim. Robust Cooperative Visual Tracking: A Combined Nonlinear Dimensionality Reduction/Robust Identification Approach. In Advances in Cooperative Control and Optimization, M. Hirsch, R. Murphey, P. Pardalos and D. Grundel, Eds., Springer Verlag, 2007. PDF BibTeX
Vlad I. Morariu and Octavia I. Camps. Modeling Correspondences in Multi-camera Tracking using Nonlinear Manifold Learning and Target Dynamics. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2006. PDF BibTeX
FIGTree - Fast Improved Gauss Transform with Tree Data Structure
A library for fast computation of Gauss transforms in multiple dimensions, using the Improved Fast Gauss Transform and Approximate Nearest Neighbor searching. The nearest neighbor searching is performed using the ANN library, available at http://www.cs.umd.edu/~mount/ANN/. This software allows for efficient computation of probabilities by Kernel Density Estimation (KDE), and can reduce complexity of algorithms commonly used in Computer Vision, Machine Learning, etc, that must evaluate the Gauss transform. The publication describing the newest improvements in the code is the NIPS 2008 paper by Morariu et al. Previous publications related to this approach are provided on Vikas Raykar's page.