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Content Provider | IEEE Xplore Digital Library |
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Author | Michaelsen, E. Muench, D. Arens, M. |
Copyright Year | 2013 |
Description | Author affiliation: Fraunhofer IOSB, Ettlingen, Germany (Michaelsen, E.; Muench, D.; Arens, M.) |
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. |
Starting Page | 206 |
Ending Page | 210 |
File Size | 2962190 |
Page Count | 5 |
File Format | |
ISBN | 9780769549903 |
ISSN | 21607516 |
DOI | 10.1109/CVPRW.2013.37 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2013-06-23 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Computer vision Algebra Image color analysis Buildings Computer graphics repetitive patters Pattern recognition Mirrors mirror-symmetry algebraic approach bottom-up search |
Content Type | Text |
Resource Type | Article |
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