1.2k citations · 4.3k across the 84 of their papers we have counts for
7 papers · 2 filters
Lensless Imaging with Compressive Ultrafast Sensing
Guy Satat, Matthew Tancik, Ramesh Raskar
Lensless imaging is an important and challenging problem. One notable solution to lensless imaging is a single pixel camera which benefits from ideas central to compressive samplin…
Deep Learning the City : Quantifying Urban Perception At A Global Scale
Abhimanyu Dubey, Nikhil Naik, Devi Parikh +2
Computer vision methods that quantify the perception of urban environment are increasingly being used to study the relationship between a city's physical appearance and the behavio…
Shape from Mixed Polarization
Vage Taamazyan, Achuta Kadambi, Ramesh Raskar
Shape from Polarization (SfP) estimates surface normals using photos captured at different polarizer rotations. Fundamentally, the SfP model assumes that light is reflected either…
Sweep Distortion Removal from THz Images via Blind Demodulation
Alireza Aghasi, Barmak Heshmat, Albert Redo-Sanchez +2
Heavy sweep distortion induced by alignments and inter-reflections of layers of a sample is a major burden in recovering 2D and 3D information in time resolved spectral imaging. Th…
Multi-velocity neural networks for gesture recognition in videos
Otkrist Gupta, Dan Raviv, Ramesh Raskar
We present a new action recognition deep neural network which adaptively learns the best action velocities in addition to the classification. While deep neural networks have reache…
Deep video gesture recognition using illumination invariants
Otkrist Gupta, Dan Raviv, Ramesh Raskar
In this paper we present architectures based on deep neural nets for gesture recognition in videos, which are invariant to local scaling. We amalgamate autoencoder and predictor ar…
Learning Gaze Transitions from Depth to Improve Video Saliency Estimation
G. Leifman, D. Rudoy, T. Swedish +2
In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing RGBD videos on regular 2D screens. We train a generative con…