Correlating Fourier descriptors of local patches for road sign recognition
2011 (English)In: IET Computer Vision, ISSN 1751-9632, E-ISSN 1751-9640, Vol. 5, no 4, 244-254 p.Article in journal (Refereed) Published
The Fourier descriptors (FDs) is a classical but still popular method for contour matching. The key idea is to apply the Fourier transform to a periodic representation of the contour, which results in a shape descriptor in the frequency domain. FDs are most commonly used to compare object silhouettes and object contours; the authors instead use this well-established machinery to describe local regions to be used in an object-recognition framework. Many approaches to matching FDs are based on the magnitude of each FD component, thus ignoring the information contained in the phase. Keeping the phase information requires us to take into account the global rotation of the contour and shifting of the contour samples. The authors show that the sum-of-squared differences of FDs can be computed without explicitly de-rotating the contours. The authors compare correlation-based matching against affine-invariant Fourier descriptors (AFDs) and WARP-matched FDs and demonstrate that correlation-based approach outperforms AFDs and WARP on real data. As a practical application the authors demonstrate the proposed correlation-based matching on a road sign recognition task.
Place, publisher, year, edition, pages
IET , 2011. Vol. 5, no 4, 244-254 p.
National CategoryEngineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-65621DOI: 10.1049/iet-cvi.2010.0040ISI: 000291385900007OAI: oai:DiVA.org:liu-65621DiVA: diva2:397235
ProjectsDIPLECS, GARNICS, ELLIIT
This paper is a postprint of a paper submitted to and accepted for publication in IET Computer Vision and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library
Fredrik Larsson, Michael Felsberg and Per-Erik Forssen, Correlating Fourier descriptors of local patches for road sign recognition, 2011, IET Computer Vision, (5), 4, 244-254.