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Multimodal saliency-based attention for object-based scene analysis

 
: Schauerte, B.; Kühn, Benjamin; Kroschel, Kristian; Stiefelhagen, Rainer

:
Postprint urn:nbn:de:0011-n-1987664 (1.2 MByte PDF)
MD5 Fingerprint: e49db3e7861791eaa6731eb6004db37c
© 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: 19.6.2012


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.1173-1179 (Vol.2)
International Conference on Intelligent Robots and Systems (IROS) <2011, San Francisco/Calif.>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
Multimodal attention is a key requirement for humanoid robots in order to navigate in complex environments and act as social, cognitive human partners. To this end, robots have to incorporate attention mechanisms that focus the processing on the potentially most relevant stimuli while controlling the sensor orientation to improve the perception of these stimuli. In this paper, we present our implementation of audio-visual saliency-based attention that we integrated in a system for knowledge-driven audio-visual scene analysis and object-based world modeling. For this purpose, we introduce a novel isophote-based method for proto-object segmentation of saliency maps, a surprise-based auditory saliency definition, and a parametric 3-D model for multimodal saliency fusion. The applicability of the proposed system is demonstrated in a series of experiments.

: http://publica.fraunhofer.de/dokumente/N-198766.html