Dr. Kang-Yu Ni Profile

Dr. Kang-Yu Ni

at HRL Labs LLC

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Publications (7)

Proceedings Article | 17 March 2015 Paper

Feature transformation of neural activity with sparse and low-rank decomposition

Kang-Yu Ni, James Benvenuto, Rajan Bhattacharyya, Rachel Millin

Proceedings Volume 9417, 94172B (2015) https://doi.org/10.1117/12.2081259

KEYWORDS: Functional magnetic resonance imaging, Brain, Feature extraction, Neuroimaging, Data modeling, Interference (communication), Electroencephalography, Visualization, Cognitive modeling, Feature selection

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Proceedings Article | 29 May 2014 Paper

SAR moving target imaging using sparse and low-rank decomposition

Kang-Yu Ni, Shankar Rao

Proceedings Volume 9077, 90771D (2014) https://doi.org/10.1117/12.2049827

KEYWORDS: Synthetic aperture radar, Target detection, Device simulation, Image processing, Image filtering, Convex optimization, Motion estimation, Image acquisition, Radar imaging, Reflectivity

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Proceedings Article | 17 September 2013 Paper

Context and task-aware knowledge-enhanced compressive imaging

Shankar Rao, Kang-Yu Ni, Yuri Owechko

Proceedings Volume 8877, 88770E (2013) https://doi.org/10.1117/12.2024594

KEYWORDS: Associative arrays, Compressed sensing, Signal to noise ratio, Chemical species, Image resolution, Roads, Sensors, Cameras, Curium, Video

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Proceedings Article | 17 September 2013 Paper

L1-based variational methods for low-power surveillance

Matthew Keegan, Kang-Yu Ni, Shankar Rao

Proceedings Volume 8877, 88770D (2013) https://doi.org/10.1117/12.2024253

KEYWORDS: Surveillance, Cameras, Video surveillance, Video, Compressed sensing, Data modeling, Video processing, Convex optimization, Imaging systems, Current controlled current source

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Proceedings Article | 4 May 2012 Paper

Accurate reconstruction of frequency-sparse signals from non-uniform samples

Kang-Yu Ni, Xiangming Kong, Roy Matic, Mohiuddin Ahmed

Proceedings Volume 8361, 836109 (2012) https://doi.org/10.1117/12.920289

KEYWORDS: Reconstruction algorithms, Compressed sensing, Detection theory, Matrices, Signal to noise ratio, Optimization (mathematics), Fourier transforms, Numerical simulations, Nickel, Roads

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Proceedings Article | 23 January 2012 Paper

Detection of unknown targets from aerial camera and extraction of simple object fingerprints for the purpose of target reacquisition

T. Nathan Mundhenk, Kang-Yu Ni, Yang Chen, Kyungnam Kim, Yuri Owechko

Proceedings Volume 8301, 83010H (2012) https://doi.org/10.1117/12.906491

KEYWORDS: Video, Cameras, Image segmentation, Target acquisition, Target detection, Data modeling, Imaging systems, Computer simulations, Performance modeling, Light sources and illumination

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An aerial multiple camera tracking paradigm needs to not only spot unknown targets and track them, but also needs to know how to handle target reacquisition as well as target handoff to other cameras in the operating theater. Here we discuss such a system which is designed to spot unknown targets, track them, segment the useful features and then create a signature fingerprint for the object so that it can be reacquired or handed off to another camera. The tracking system spots unknown objects by subtracting background motion from observed motion allowing it to find targets in motion, even if the camera platform itself is moving. The area of motion is then matched to segmented regions returned by the EDISON mean shift segmentation tool. Whole segments which have common motion and which are contiguous to each other are grouped into a master object. Once master objects are formed, we have a tight bound on which to extract features for the purpose of forming a fingerprint. This is done using color and simple entropy features. These can be placed into a myriad of different fingerprints. To keep data transmission and storage size low for camera handoff of targets, we try several different simple techniques. These include Histogram, Spatiogram and Single Gaussian Model. These are tested by simulating a very large number of target losses in six videos over an interval of 1000 frames each from the DARPA VIVID video set. Since the fingerprints are very simple, they are not expected to be valid for long periods of time. As such, we test the shelf life of fingerprints. This is how long a fingerprint is good for when stored away between target appearances. Shelf life gives us a second metric of goodness and tells us if a fingerprint method has better accuracy over longer periods. In videos which contain multiple vehicle occlusions and vehicles of highly similar appearance we obtain a reacquisition rate for automobiles of over 80% using the simple single Gaussian model compared with the null hypothesis of <20%. Additionally, the performance for fingerprints stays well above the null hypothesis for as much as 800 frames. Thus, a simple and highly compact single Gaussian model is useful for target reacquisition. Since the model is agnostic to view point and object size, it is expected to perform as well on a test of target handoff. Since some of the performance degradation is due to problems with the initial target acquisition and tracking, the simple Gaussian model may perform even better with an improved initial acquisition technique. Also, since the model makes no assumption about the object to be tracked, it should be possible to use it to fingerprint a multitude of objects, not just cars. Further accuracy may be obtained by creating manifolds of objects from multiple samples.

Proceedings Article | 30 October 2009 Paper

Image reconstruction by deterministic compressed sensing with chirp matrices

Kangyu Ni, Prasun Mahanti, Somantika Datta, Svetlana Roudenko, Douglas Cochran

Proceedings Volume 7497, 74971S (2009) https://doi.org/10.1117/12.832649

KEYWORDS: Reconstruction algorithms, Compressed sensing, Matrices, Image restoration, Wavelets, Medical imaging, Radon, Image compression, Magnetic resonance imaging, Optimization (mathematics)

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