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André Sobiecki

dblp:130/1623 · also André C. Sobiecki · DBLP profile ↗
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3ranked-venue papers
2as first author
1since 2021 · last 2021
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

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%

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

TopicWeightPapersLastEvidence papers
Geometric modeling and processing › shape representation
multiscale shape representation
0.212016
An Unified Multiscale Framework for Planar, Surface, and Curve Skeletonization · IEEE Trans. Pattern Anal. Mach. Intell. 2016
Geometric modeling and processing
shape analysis
0.212016
An Unified Multiscale Framework for Planar, Surface, and Curve Skeletonization · IEEE Trans. Pattern Anal. Mach. Intell. 2016
Geometric modeling and processing
skeletonization
0.212016
An Unified Multiscale Framework for Planar, Surface, and Curve Skeletonization · IEEE Trans. Pattern Anal. Mach. Intell. 2016

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

numerical simulation · 0.2mass transport · 0.2
YearPublicationVenuePosition
2021 Does face restoration improve face verification?
André Sobiecki, Julius van Dijk, Hidde Folkertsma, Alexandru C. Telea
Multim. Tools Appl.1
2016 An Unified Multiscale Framework for Planar, Surface, and Curve Skeletonization
abstract
Computing skeletons of 2D shapes, and medial surface and curve skeletons of 3D shapes, is a challenging task. In particular, there is no unified framework that detects all types of skeletons using a single model, and also produces a multiscale representation which allows to progressively simplify, or regularize, all skeleton types. In this paper, we present such a framework. We model skeleton detection and regularization by a conservative mass transport process from a shape's boundary to its surface skeleton, next to its curve skeleton, and finally to the shape center. The resulting density field can be thresholded to obtain a multiscale representation of progressively simplified surface, or curve, skeletons. We detail a numerical implementation of our framework which is demonstrably stable and has high computational efficiency. We demonstrate our framework on several complex 2D and 3D shapes.
Andrei C. Jalba, André Sobiecki, Alexandru C. Telea
IEEE Trans. Pattern Anal. Mach. Intell.2
2014 Comparison of curve and surface skeletonization methods for voxel shapes
André Sobiecki, Andrei C. Jalba, Alexandru C. Telea
Pattern Recognit. Lett.1