Feature detection with an improved anisotropic filter

Authors:
Mohamed Gobara;David Suter
Affiliations:
Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia;Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
Venue:
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Year:
2006

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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.