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Ken Nawyn

dblp:87/4977 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1994
—ORCID · none

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

Graphics, 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
Image and video processing · 100%

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

TopicWeightPapersLastEvidence papers
Image and video processing › image registration
deformable image registration
0.011994
Three-dimensional elastic matching of volumes · IEEE Trans. Image Process. 1994
Image and video processing › image registration
elastic matching
0.011994
Three-dimensional elastic matching of volumes · IEEE Trans. Image Process. 1994
Image and video processing
image registration
0.011994
Three-dimensional elastic matching of volumes · IEEE Trans. Image Process. 1994

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

warping · 0.0triangular patch surface models · 0.0active contours · 0.0
YearPublicationVenuePosition
1994 Three-dimensional elastic matching of volumes
abstract
Registering volumes that have been deformed with respect to each other involves recovery of the deformation. A 3-D elastic matching algorithm has been developed to use surface information for registering volumes. Surface extraction is performed in two steps: extraction of contours in 2-D image planes using active contours, and forming triangular patch surface models from the stack of 2-D contours. One volume is modeled as being deformed with respect to another goal volume. Correspondences between surfaces in the two image volumes are used to warp the deformed volume towards its goal. This process of contour extraction, surface formation and matching, and warping is repeated a number of times, with decreasing image volume stiffness. As the iterations continue the stretched volume is refined towards its goal volume. Registration examples of deformed volumes are presented.
Mehran Moshfeghi, Surendra Ranganath, Ken Nawyn
IEEE Trans. Image Process.3