Paper
28 May 2004 Template gradient matching in spherical images
Author Affiliations +
Proceedings Volume 5298, Image Processing: Algorithms and Systems III; (2004) https://doi.org/10.1117/12.527043
Event: Electronic Imaging 2004, 2004, San Jose, California, United States
Abstract
Most of today's robot vehicles are equipped with omnidirectional sensors which provide surround awareness and easier navigation. Due to the persistence of the appearance in omnidirectional images, many global navigation or formation control tasks, instead of using landmarks or fiducials, they need only reference images of target positions or objects. In this paper, we study the problem of template matching in spherical images. The natural transformation of a pattern on the sphere is a 3D rotation and template matching is the localization of a target in any orientation given by a reference image. Unfortunately, the support of the template is space variant on the Euler angle parameterization. Here we propose a new method which matches the gradients of the image and the template, with space-invariant operation. Using properties of the angular momentum, we have proved in fact that the gradient correlation can be very easily computed by the 3D Inverse Fourier Transform of a linear combination of spherical harmonics. An exhaustive search localizes the maximum of this correlation. Experimental results on real data show a very accurate localization with a variety of targets. In future work, we plan to address targets appearing in different scales.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lorenzo Sorgi and Kostas Daniilidis "Template gradient matching in spherical images", Proc. SPIE 5298, Image Processing: Algorithms and Systems III, (28 May 2004); https://doi.org/10.1117/12.527043
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Cited by 7 scholarly publications.
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KEYWORDS
Spherical lenses

Optical spheres

Combined lens-mirror systems

Image processing

Fourier transforms

3D image processing

3D acquisition

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