Avital Kelman, Michal Sofka and Charles V. Stewart
Proceedings of the IEEE Computer Society Workshop on Image Registration and Fusion (in conjunction with IEEE CVPR), 2007.
Abstract:
In this paper, we investigate the effect of
substantial inter-image intensity changes and
changes in modality on the performance of keypoint
detection, description, and matching algorithms in
the context of image registration. In doing so, we
modify widely-used keypoint descriptors such as SIFT
and shape contexts, attempting to capture the
insight that some structural information is indeed
preserved between images despite dramatic appearance
changes. These extensions include (a) pairing
opposite-direction gradients in the formation of
orientation histograms and (b) focusing on edge
structures only. We also compare the stability of
MSER, Laplacian-of-Gaussian, and Harris corner
keypoint location detection and the impact of
detection errors on matching results. Our
experiments on multimodal image pairs and on image
pairs with significant intensity differences show
that indexing based on our modified descriptors
produces more correct matches on difficult pairs
than current techniques at the cost of a small
decrease in performance on easier pairs. This
extends the applicability of image registration
algorithms such as the Dual-Bootstrap which rely on
correctly matching only a small number of keypoints.
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