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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)
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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
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) <2013, Portland/Or.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

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.