SHREC 2011: robust feature detection and description benchmark
Edmond Boyer
(1)
,
Alexander M. Bronstein
(2)
,
Michael M. Bronstein
(3)
,
Benjamin Bustos
(4)
,
Tal Darom
(5)
,
Radu Horaud
(6)
,
Ingrid Hotz
(7)
,
Yosi Keller
(5)
,
Johannes Keustermans
(8)
,
Artiom Kovnatsky
(9)
,
Roee Litman
(2)
,
Jan Reininghaus
(7)
,
Ivan Sipiran
(4)
,
Dirk Smeets
(8)
,
Paul Suetens
(8)
,
Dirk Vandermeulen
(8)
,
Andrei Zaharescu
(10)
,
Valentin Zobel
(7)
1
MORPHEO -
Capture and Analysis of Shapes in Motion
2 Department of Electrical Engineering
3 Faculty of Informatics [Lugano]
4 Universidad de Chile - Department of Computer Science
5 Faculty of Engineering
6 PERCEPTION - Interpretation and Modelling of Images and Videos
7 ZIB - Zuse Institute Berlin
8 ESAT/SCD-COSIC - Department of Electrical Engineering - K.U.Leuven
9 TECHNION - Department of Mathematics
10 Aimetis Corp.
2 Department of Electrical Engineering
3 Faculty of Informatics [Lugano]
4 Universidad de Chile - Department of Computer Science
5 Faculty of Engineering
6 PERCEPTION - Interpretation and Modelling of Images and Videos
7 ZIB - Zuse Institute Berlin
8 ESAT/SCD-COSIC - Department of Electrical Engineering - K.U.Leuven
9 TECHNION - Department of Mathematics
10 Aimetis Corp.
Edmond Boyer
- Fonction : Auteur
- PersonId : 752316
- IdHAL : edmond-boyer
- ORCID : 0000-0002-1182-3729
- IdRef : 108147797
Radu Horaud
- Fonction : Auteur
- PersonId : 16183
- IdHAL : radu-horaud
- ORCID : 0000-0001-5232-024X
- IdRef : 032302495
Résumé
Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC'11 robust feature detection and description benchmark simulates the feature detection and description stages of feature-based shape retrieval algorithms. The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of transformations. The benchmark allows evaluating how algorithms cope with certain classes of transformations and strength of the transformations that can be dealt with. The present paper is a report of the SHREC'11 robust feature detection and description benchmark results.