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Erin Edmiston

dblp:38/7659 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
0000-0002-3548-6026ORCID · reported

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

Artificial 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.

Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing
shape analysis
0.112009
Shape comparison using perturbing shape registration · CVPR 2009
Geometric modeling and processing › shape analysis
shape comparison
0.112009
Shape comparison using perturbing shape registration · CVPR 2009
Geometric modeling and processing
shape registration
0.112009
Shape comparison using perturbing shape registration · CVPR 2009
Geometric modeling and processing › shape analysis
statistical shape analysis
0.112009
Shape comparison using perturbing shape registration · CVPR 2009

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

resampling · 0.2perturbation scheme · 0.2aggregation · 0.2
YearPublicationVenuePosition
2009 Shape comparison using perturbing shape registration
abstract
Shape registration is often involved in computing statistical differences between groups of shapes, which is a key aspect of morphometric study. The results of shape difference are found to be sensitive to registration, i.e., different registration methods lead to varied results. This raises the question of how to improve the reliability of registration procedures. This paper proposes a perturbation scheme, which perturbs registrations by feeding them with different resampled shape groups, and then aggregates the resulting shape differences. Experiments are conducted using three typical registration algorithms on both synthetic and biomedical shapes, where more reliable inter-group shape differences are found under the proposed scheme.
Yifeng Jiang 0001, Erin Edmiston, Fei Wang 0003, Hilary P. Blumberg, Lawrence H. Staib, Xenophon Papademetris
CVPR2