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Frederik J. Timmermans

dblp:38/2221 · 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

Artificial intelligence and machine learning · 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%

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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › 3d shape analysis › geometric attribute estimation
curvature estimation
0.012004
On Curvature Estimation of ISO Surfaces in 3D Gray-Value Images and the Computation of Shape Descriptors · IEEE Trans. Pattern Anal. Mach. Intell. 2004
Computer vision › 3D vision › 3d shape representation
shape descriptor
0.012004
On Curvature Estimation of ISO Surfaces in 3D Gray-Value Images and the Computation of Shape Descriptors · IEEE Trans. Pattern Anal. Mach. Intell. 2004

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

knutsson mapping · 0.0gradient structure tensor · 0.0bending energy · 0.0
YearPublicationVenuePosition
2004 On Curvature Estimation of ISO Surfaces in 3D Gray-Value Images and the Computation of Shape Descriptors
abstract
In this paper, we present a novel method to estimate curvature of iso gray-level surfaces in gray-value images. Our method succeeds where standard isophote curvature estimation methods fail. There is neither a segmentation of the surface needed nor a parametric model assumed. Our estimator works on the orientation field of the surface. This orientation field and a description of local structure is obtained by the Gradient Structure Tensor. The estimated orientation field has discontinuities mod pi. It is mapped via the Knutsson mapping to a continuous representation. The principal curvatures of the surface, a coordinate invariant property, are computed in this mapped representation. From these curvatures, locally the bending energy is computed to describe the surface shape. An extensive evaluation shows that our curvature estimation is robust even in the presence of noise, independent of the scale of the object and furthermore the relative error stays small.
Bernd Rieger, Frederik J. Timmermans, Lucas J. van Vliet, Piet W. Verbeek
IEEE Trans. Pattern Anal. Mach. Intell.2