This page summarises IAT tutorials. Tutorials along with the demos help the users to use the toolbox and to align images. Enjoy!

Area-based (direct) alignment

Area-based alignment methods, a.k.a. direct methods [3], rely on the following brightness constancy assumption: where warps the coordinates of ‘s plane. Two methods that are based on this assumption is the Lucas-Kanade algorithm [1] and the Enhanced Correlation Coefficient (ECC) algorithm [2] (see [2] for their differences). In short, both optimize a criterion to find …

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Feature-based alignment

Unlike direct methods, feature-based alignment relies on feature correspondences between images. Commonly, a detector detects interest points in images, while a descriptor describes the area around these keypoints. A matching scheme then extracts correspondences between keypoints based on similarities between the respective descriptors. The optimum transform that explains these correspondences, up to some tolerance, is …

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Feature-based Vs direct alignment

Both feature-based and area-based alignment have their own pros and cons. In principle, feature-based matching is invariant to the strength of the geometric deformation, while it may fail when the image content is weakly-textured, periodic etc. On the other hand, area-based (direct) alignment methods are able to align such images, while they need a good …

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Non-rigid alignment with SIFT-flow

SIFTflow was primarily designed for matching similar images of different scenes [1]. However, it can be easily used when images of the same scene, that undergo non-rigid deformation, must be registered. The algorithm estimates the optimum flows that minimize an energy criterion, while the energy minimisation is done by a multi-layer belief propagation scheme.┬áIn what …

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