Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Recognition of symmetry structure by use of gestalt algebra

 
: Michaelsen, Eckart; Münch, David; Arens, Michael

:
Postprint urn:nbn:de:0011-n-2486736 (2.8 MByte PDF)
MD5 Fingerprint: 5bc0de234db452fa2eca7d8d7ae89df4
© 2013 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: 11.7.2013


IEEE Computer Society; Institute of Electrical and Electronics Engineers -IEEE-:
IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR 2013. Vol.1 : 23.06.2013-28.06.2013, Portland, Oregon, USA
New York, NY: IEEE, 2013
ISBN: 978-0-7695-4990-3
ISBN: 978-1-4799-0994-0
S.206-210
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) <2013, Portland/Or.>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
While most approaches to symmetry detection in machine vision try to explain the gray-values or colors of the pixels, Gestalt algebra has no room for such measurement data. The entities (i.e. Gestalten) are only defined with respect to each other. They form a generic hierarchy, and live in a continuous domain without any pixel raster. There is also no constraint forcing them to completely fill an image, or prohibiting overlap. Yet, when used as a tool for symmetry recognition, the algebra must be somehow connected to the given data. In this paper this is done only on the primitive level using the well-known SIFT feature detector. From a set of such SIFT-based Gestalten follows a combinatorial set of higher-order symmetric Gestalten by constructing all possible terms using the operations of the algebra. The Gestalt domain contains a quality or assessment dimension. Taking the best Gestalten with respect to this attribute and clustering them yields the output for this competition participation.

: http://publica.fraunhofer.de/dokumente/N-248673.html