Image matching using generalized scale-space interest points

Tony Lindeberg

Proceedings of SSVM 2013 - Scale-Space and Variational Methods in Computer Vision, Springer Lecture Notes in Computer Science, volume 7893, pages 355-367, 2013. Extended version in Journal of Mathematical Imaging and Vision, volume 52, number 1, pages 3-36, 2015.

Abstract

The performance of matching and object recognition methods based on interest points depends on both the properties of the underlying interest points and the associated image descriptors. This paper demonstrates the advantages of using generalized scale-space interest point detectors when computing image descriptors for image-based matching. These generalized scale-space interest points are based on linking of image features over scale and scale selection by weighted averaging along feature trajectories over scale and allow for a higher ratio of correct matches and a lower ratio of false matches compared to previously known interest point detectors within the same class. Specifically, it is shown how a significant increase in matching performance can be obtained in relation to the underlying interest point detectors in the SIFT and the SURF operators. We propose that these generalized scale-space interest points when accompanied by associated scale-invariant image descriptors should allow for better performance of interest point based methods for image-based matching, object recognition and related vision tasks.

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On-line version: (At the official site of Springer)

Extended journal paper: (PDF 11.9 Mb)

Background and related material: (Extended theory in journal paper) (Theoretical paper on scale selection properties of generalized scale-space interest points) (Tutorial article on the use of Laplacian interest points and their difference of Gaussians approximation for computing locally scale adapted SIFT descriptors) (Earlier paper on Laplacian and determinant of the Hessian interest points with automatic scale selection)

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