Displaying One Session

08/20/2019 01:15 PM - 03:15 PM Sandpiper C/D
Time
01:15 PM - 03:15 PM
OIST - Optical Imaging and Sensing Technology

TuB3.1 - COMPRESSIVE DEEP LEARNING APPROACHES FOR INFRARED AND HYPERSPECTRAL MACHINE VISION

Presentation Type
Invited Submission
Date
08/20/2019
Time
01:15 PM - 03:15 PM
Room
Sandpiper C/D
Duration
30 Minutes
Lecture Time
01:15 PM - 01:45 PM

Abstract

Abstract

This talk reviews approaches to utilize the single pixel camera for specific machine vision tasks directly on compressive measurements. Included are simulation and hardware results of coupling an optical modulator to small infrared focal plane arrays and visible spectrometers to perform high-resolution object recognition.

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OIST - Optical Imaging and Sensing Technology

TuB3.2 - ASSURED CAPTURE OF TRANSIENT RF EVENTS ACROSS EXTREMELY WIDE BANDWIDTHS

Presentation Type
Invited Submission
Date
08/20/2019
Time
01:15 PM - 03:15 PM
Room
Sandpiper C/D
Duration
30 Minutes
Lecture Time
01:45 PM - 02:15 PM

Abstract

Abstract

The combination of wideband spectrum sensing, optical RF signal buffering, low-latency digital processing, and fast-tuning digital RF receivers enables reliable capture of transient RF events across extremely wide bandwidths for comprehensive situational awareness in electronic warfare and other defense applications.

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TuB3.3 - SPARSE-APERTURE QUALITATIVE INVERSE SCATTERING USING A PHASE-DELAY-BASED FREQUENCY VARIATION CONSTRAINT

Presentation Type
Contributed Submission
Date
08/20/2019
Time
01:15 PM - 03:15 PM
Room
Sandpiper C/D
Duration
15 Minutes
Lecture Time
02:15 PM - 02:30 PM

Abstract

Abstract

We introduce a sparse-aperture variant of the Linear Sampling Method, which is a qualitative inverse scattering technique for reconstructing target shape. The technique reduces imaging artifacts arising from sparse data collections by incorporating a priori knowledge of propagation-based phase-delay into the inversion.

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OIST - Optical Imaging and Sensing Technology

TuB3.4 - FOURIER OPTICS COPROCESSOR FOR IMAGE PROCESSING AND CONVOLUTIONAL NEURAL NETWORK

Presentation Type
Contributed Submission
Date
08/20/2019
Time
01:15 PM - 03:15 PM
Room
Sandpiper C/D
Duration
15 Minutes
Lecture Time
02:30 PM - 02:45 PM

Abstract

Abstract

Convolution Neural Networks (CNN) are artificial networks able to extract features from large dataset by spatial filtering. Here we propose an optical coprocessor able to perform large image filtering and convolutions based on a two stage 4F system and digital micromirror arrays, outperforming current architectures.

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OIST - Optical Imaging and Sensing Technology

TuB3.5 - ARBITRARY FOVEATION IN COMPRESSIVE IMAGING UTILIZING THE STONE TRANSFORM

Presentation Type
Contributed Submission
Date
08/20/2019
Time
01:15 PM - 03:15 PM
Room
Sandpiper C/D
Duration
15 Minutes
Lecture Time
02:45 PM - 03:00 PM

Abstract

Abstract

We demonstrate a method of compressive single-pixel imaging that allows for spatial foveation anywhere in the image, determined after data acquisition, using the Sum-To-One Transform. We also show a novel method of generating fast L2 previews from Sum-To-One pattern measurements.

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OIST - Optical Imaging and Sensing Technology

TuB3.6 - Sparsity in computer generated radar imagery

Presentation Type
Invited Submission
Date
08/20/2019
Time
01:15 PM - 03:15 PM
Room
Sandpiper C/D
Duration
15 Minutes
Lecture Time
03:00 PM - 03:15 PM

Abstract

Abstract

We describe a method for generating detailed synthetic radar imagery using single-frequency computational electromagnetics simulations. We use this technique to study the sparsity in the full k-space (i.e., spatial frequency) domain, rather than in the frequency or spatial domains.
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