NEWS
CVPR 2022!
GlideNet for Multi-Category Attributes Prediction
Attaching attributes (such as color, shape, state, action) to object categories is an important computer vision problem. Attribute prediction has seen exciting recent progress and is often formulated as a multi-label classification problem. Yet significant challenges remain in: 1) predicting a large number of attributes over multiple object categories, 2) modeling category-dependence of attributes, 3) methodically capturing both global and local scene context, and 4) robustly predicting attributes of objects with low pixel-count ... Read More
New tutorial at 2021 IEEE RadarCon
Convex Optimization for Adaptive Radar.
For more details about iPAL research in radar systems click here.
NEW! Feature Article in IEEE Signal Processing Magazine
Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing by V. Monga, Y. Li and Y. Eldar.
The article can be found here.
NEW! Handbook of Convex Optimization Methods in Imaging Science
This book discusses imaging science and provides tools for solving image processing and computer vision problems using convex optimization methods. Throughout the handbook, the authors introduce topics on the most key aspects of image acquisition and processing that are based on the formulation and solution of novel optimization problems.
RESEARCH AREAS
KEY RESEARCH COLLABORATORS
Academia
Prof. Yonina Eldar (Weizmann Institute, Israel) and Prof. Trac Tran (Johns Hopkins University)
Government Labs
Dr. Muralidhar Rangaswamy (US Air Force Research Laboratory)
Dr. Nasser Nasrabadi (US Army Research Laboratory, now West Virginia University)
Industry
Dr. Raja Bala (Prev. Xerox PARC, Now Amazon)
KEY RESEARCH SPONSORS
Email
ipal.psu@gmail.com
Address
104 Electrical Engineering East,
University Park, PA 16802, USA
Lab Phone:
814-863-7810
814-867-4564
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