Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

A rotation invariant feature descriptor O-DAISY and its FPGA implementation

 
: Fischer, Jan; Ruppel, Alexander; Weisshardt, Florian; Verl, Alexander

:
Postprint urn:nbn:de:0011-n-1909539 (1.3 MByte PDF)
MD5 Fingerprint: a570ccd4c1e38afa1851bbc9f21610af
© 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Erstellt am: 21.12.2011


Amato, N.M. ; Institute of Electrical and Electronics Engineers -IEEE-; Robotics Society of Japan -RSJ-:
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011 : San Francisco, California, USA, 25 - 30 September 2011
Piscataway/NJ: IEEE, 2011
ISBN: 978-1-61284-455-8
ISBN: 978-1-61284-454-1
S.2365-2370 (Vol.3)
International Conference on Intelligent Robots and Systems (IROS) <2011, San Francisco/Calif.>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPA ()
rotation invariance; Roboter; Objekterkennung

Abstract
State-of-the-art local feature descriptors like SIFT or SURF require a significant amount of computational power which prevents their usage in applications with real time constraints. Despite recent efforts to simplify the calculation of feature descriptors, a faster computation comes often to the disadvantage of weakening the invariance to rotation or scale. Recently, Tola et al. introduced DAISY, a new local feature descriptor for wide-baseline matching across stereo image pairs. It is shown that DAISY outperforms SIFT in terms of matching accuracy while being computed significantly faster. This paper takes on the idea of DAISY by proposing a rotational invariant extension of the descriptor, called O-DAISY, and outlining its implementation on FPGA to achieve real time performance. The results are benchmarked against its original version and against the widely used descriptors BRIEF and SURF on a standardized image set.

: http://publica.fraunhofer.de/dokumente/N-190953.html