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Herbert Bay

dblp:b/HerbertBay · DBLP profile ↗
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4ranked-venue papers
3as first author
0since 2021 · last 2008
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 2 first-authorApplied, 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
2 papers
3D vision · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › feature matching
local feature detection and description
0.112006
SURF: Speeded Up Robust Features · ECCV (1) 2006
Computer vision › 3D vision › stereo vision
stereo matching
0.112005
Wide-Baseline Stereo Matching with Line Segments · CVPR (1) 2005
Computer vision › 3D vision › feature matching › line matching
wide-baseline line matching
0.112005
Wide-Baseline Stereo Matching with Line Segments · CVPR (1) 2005
Computer vision › 3D vision
feature matching
0.012006
SURF: Speeded Up Robust Features · ECCV (1) 2006
Computer vision › 3D vision › multi-view geometry › epipolar geometry estimation
fundamental matrix estimation
0.012005
Wide-Baseline Stereo Matching with Line Segments · CVPR (1) 2005

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

topological consistency · 0.1coplanar grouping · 0.1
YearPublicationVenuePosition
2008 Speeded-Up Robust Features (SURF)
Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool
Comput. Vis. Image Underst.1
2006 SURF: Speeded Up Robust Features
Herbert Bay, Tinne Tuytelaars, Luc Van Gool
ECCV (1)1
2006 Retina Mosaicing Using Local Features
Philippe C. Cattin, Herbert Bay, Luc Van Gool, Gábor Székely
MICCAI (2)2
2005 Wide-Baseline Stereo Matching with Line Segments
abstract
We present a new method for matching line segments between two uncalibrated wide-baseline images. Most current techniques for wide-baseline matching are based on viewpoint invariant regions. Those methods work well with highly textured scenes, but fail with poorly textured ones. We show that such scenes can be successfully matched using line segments. Moreover, since line segments and regions provide complementary information, their combined matching allows to deal with a broader range of scenes. We generate an initial set of line segment correspondences, and then iteratively increase their number by adding matches consistent with the topological structure of the current ones. Finally, a coplanar grouping stage allows to estimate the fundamental matrix even from line segments only.
Herbert Bay, Vittorio Ferrari, Luc Van Gool
CVPR (1)1