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Karen Roberts

dblp:67/3299 · DBLP profile ↗
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5ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · conflict

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

Security and privacy · 4Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

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.

Artificial intelligence
1 paper
Robot navigation and mapping · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › active vision
gaze stabilization
0.011993
Centering behavior using peripheral vision · CVPR 1993
Robotics › Robot navigation and mapping
obstacle avoidance
0.011993
Centering behavior using peripheral vision · CVPR 1993

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

optical flow · 0.0negative feedback control · 0.0
YearPublicationVenuePosition
2010 EU update
Helen Kemmitt, Michael Dizon, Karen Roberts, Bianca Chouls
Comput. Law Secur. Rev.3
2010 EU update
Helen Kemmitt, Michael Dizon, Karen Roberts, Alex Clayton, Elizabeth Grimwood-Taylor
Comput. Law Secur. Rev.3
2010 EU update
Helen Kemmitt, Michael Dizon, Karen Roberts, Liz Grimwood-Taylor, Peter O'Byrne, Paul Rawlinson
Comput. Law Secur. Rev.3
2010 Baker & McKenzie's annual review of developments in EU law relating to IP, IT & telecommunications
Helen Kemmitt, Karen Roberts, Benjamin Slinn, Marianna Rybynok
Comput. Law Secur. Rev.2
1993 Centering behavior using peripheral vision
abstract
The ability to control egomotion using low resolution peripheral vision is crucial for enabling a small high resolution fovea to attend to features that require detailed examination. The robot described demonstrates the ability to use low resolution motion vision over large fields of view to steer between obstacles. The system uses the maximum flow observed in left and right peripheral visual fields to indicate obstacle proximity. Each peripheral field constitutes one-third of a wide-angle lens. The left and right proximates are compared to steer through the gap. Negative feedback control of steering is able to tolerate inaccuracies in the signal estimation. This interpretation of the flows is based on the assumption that the camera is translating along the gaze vector. This condition is maintained under egomotion by active gaze stabilization. Head rotation is countered by eye rotation, and gaze is returned to the heading by rapid camera movements when necessary.>
David Coombs, Karen Roberts
CVPR2