Abstract
The problem of detecting local image features that are invariant to scale, orientation, illumination and viewpoint changes is a critical issue in many computer vision applications. The challenges involve localizing the image features accurately in the spatial and frequency domains and describing them with a stable analytical representation. In this paper we address these two issues by proposing a new non-linear scale-space implementation that improves the localization accuracy of the SIFT [3] local features. Furthermore we propose a simple adjustment to the standard SIFT descriptor and show that the modified version is more robust to affine changes.
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Keywords
- Receiver Operating Characteristic
- Receiver Operating Characteristic Curve
- Interest Point
- Sample Region
- Sift Descriptor
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© 2006 Springer-Verlag Berlin Heidelberg
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Gobara, M., Suter, D. (2006). Feature Detection with an Improved Anisotropic Filter. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_64
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DOI: https://doi.org/10.1007/11612704_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31244-4
Online ISBN: 978-3-540-32432-4
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