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Reconstruction-free inference on compressive measurements
Suhas Lohit
, Kuldeep Kulkarni
,
Pavan Turaga
, Jian Wang
, Aswin C. Sankaranarayanan
Arts, Media and Engineering, School of (AME)
Sensor, Signal and Information Processing Center (SenSIP)
Electrical, Computer, and Energy Engineering, School of (IAFSE-ECEE)
Electrical Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
50
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Scopus citations
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Keyphrases
Face Recognition
100%
Compressive Measurement
100%
Reconstruction-free Inference
100%
Image Reconstruction
66%
Single Camera
66%
Short-wave Infrared
33%
Order of Accuracy
33%
Theoretical Development
33%
Imaging Device
33%
Visible Spectrum
33%
Action Recognition
33%
Inference Problem
33%
High Signal-to-noise Ratio
33%
Face Database
33%
High Compression Ratio
33%
Inference Tasks
33%
Inner Product
33%
Accurate Reconstruction
33%
Nonlinear Features
33%
Smashed Filter
33%
Popular Architecture
33%
Visual Inference
33%
Spatial Mask
33%
Compressive Domain
33%
High-dimensional Signals
33%
Nyquist Sampling Rate
33%
Small Losses
33%
Pseudorandom
33%
Spatial multiplexing
33%
Measurement Rate
33%
Spatial Multiplexing Cameras
33%
Sub-Nyquist Sampling
33%
Computer Science
Facial Recognition
100%
Image Reconstruction
66%
Spatial Multiplexing
66%
Theoretical Development
33%
Visible Spectrum
33%
Compression Ratio
33%
Action Recognition
33%
Sampling Rate
33%
Inference Task
33%
Linear Feature
33%
Nyquist Frequency
33%
Signal-to-Noise Ratio
33%