Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/56252
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Type: Conference paper
Title: Feature detection with an improved anisotropic filter.
Author: Gobara, M.
Suter, D.
Citation: Computer Vision – ACCV 2006: 7th Asian Conference on Computer Vision Hyderabad, India, January 13-16, 2006, Proceedings, Part II / P.J. Narayanan, Shree K. Nayar, Heung-Yeung Shum (eds.), pp.643-652
Publisher: Springer
Publisher Place: Berlin
Issue Date: 2006
Series/Report no.: Lecture Notes in Computer Science, 2006; 3852: 643-652
ISBN: 3-540-31244-7
9783540312444
ISSN: 0302-9743
1611-3349
Conference Name: Asian Conference on Computer Vision (7th : 2006 : Hyderabad, India)
Statement of
Responsibility: 
Mohamed Gobara and David Suter
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.
Description: © Springer-Verlag Berlin Heidelberg 2006
RMID: 0020094153
DOI: 10.1007/11612704_64
Appears in Collections:Computer Science publications

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