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James M. Varah

dblp:96/201 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 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
3D vision · 100%
Theoretical computer science
1 paper
Mathematical optimization · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › 3d shape modeling
deformable object modeling
0.012004
Local compliance estimation via positive semidefinite constrained least squares · IEEE Trans. Robotics 2004
Mathematical optimization › least squares
constrained least squares
0.012004
Local compliance estimation via positive semidefinite constrained least squares · IEEE Trans. Robotics 2004

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

positive semidefinite constrained least squares · 0.1
YearPublicationVenuePosition
2004 Local compliance estimation via positive semidefinite constrained least squares
abstract
We present a method to estimate a positive semidefinite matrix by linear least squares, and we apply this method to the estimation of local compliance matrices during deformable object modeling. Estimation of physical quantities from measurements has to consider noise due to measurement and modeling inaccuracy. Enforcing constraints during estimation can guarantee physically plausible results even under difficult measurement conditions.
Nathan Krislock, Jochen Lang 0001, James M. Varah, Dinesh K. Pai, Hans-Peter Seidel
IEEE Trans. Robotics3