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Nicholas W. Roberts

dblp:151/4883 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2014
0000-0002-4540-6683ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
2 papers
Computational photography and imaging · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%
Computer networks
1 paper
Optical networks · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging
polarization imaging
0.422014
Bioinspired Polarization Imaging Sensors: From Circuits and Optics to Signal Processing Algorithms and Biomedical Applications · Proc. IEEE 2014
Animal Polarization Imaging and Implications for Optical Processing · Proc. IEEE 2014
Computational photography and imaging › image signal processing
image sensor processing
0.212014
Bioinspired Polarization Imaging Sensors: From Circuits and Optics to Signal Processing Algorithms and Biomedical Applications · Proc. IEEE 2014
Optical networks
optical signal processing
0.112014
Animal Polarization Imaging and Implications for Optical Processing · Proc. IEEE 2014

Methods — techniques the papers use, named apart from their topics

signal processing algorithms · 0.4polarization imaging · 0.4bio-inspired models · 0.4CMOS image sensor design · 0.4
YearPublicationVenuePosition
2014 Animal Polarization Imaging and Implications for Optical Processing
abstract
Biologically inspired solutions for modern-day sensory systems promise to deliver both higher capacity and faster, more efficient processing of information than current computational approaches. Many animals are able to perform remarkable sensing tasks despite only being able to process what would be considered modest data rates and bandwidths. The key biological innovations revolve around dedicated filter designs. By sacrificing some flexibility, specifically matched and hard-wired sensory systems, designed primarily for single roles, provide a blueprint for data and task-specific efficiency. In this paper, we examine several animal visual systems designed to use the polarization of light in spatial imaging. We investigate some implications for artificial optical processing based on models of polarization image processing in fiddler crabs, cuttlefish, octopus, and mantis shrimp.
Nicholas W. Roberts, Martin J. How, Megan L. Porter, Shelby E. Temple, Roy L. Caldwell, Samuel B. Powell, Viktor Gruev, N. Justin Marshall, Thomas W. Cronin
Proc. IEEE1
2014 Bioinspired Polarization Imaging Sensors: From Circuits and Optics to Signal Processing Algorithms and Biomedical Applications
abstract
In this paper, we present recent work on bioinspired polarization imaging sensors and their applications in biomedicine. In particular, we focus on three different aspects of these sensors. First, we describe the electro-optical challenges in realizing a bioinspired polarization imager, and in particular, we provide a detailed description of a recent low-power complementary metal-oxide-semiconductor (CMOS) polarization imager. Second, we focus on signal processing algorithms tailored for this new class of bioinspired polarization imaging sensors, such as calibration and interpolation. Third, the emergence of these sensors has enabled rapid progress in characterizing polarization signals and environmental parameters in nature, as well as several biomedical areas, such as label-free optical neural recording, dynamic tissue strength analysis, and early diagnosis of flat cancerous lesions in a murine colorectal tumor model. We highlight results obtained from these three areas and discuss future applications for these sensors.
Timothy York, Samuel B. Powell, Shengkui Gao, Lindsey Kahan, Tauseef Charanya, Debajit Saha, Nicholas W. Roberts, Thomas W. Cronin, N. Justin Marshall, Samuel Achilefu, Spencer P. Lake, Baranidharan Raman, Viktor Gruev
Proc. IEEE7