Image registration algorithms are usually based on point features such as SIFT (Scale-Invariant Feature Transform).
I saw some references to line features, but I was wondering if it would be possible to match image segments instead of points. For example, given source and transformed image:
I can do edge detection, blurring and Watershed Transform on each:
Regrettably, the segmentation turned out to be too different on each image to match individual segments.
I saw some papers on matching shapes and shape descriptors which are invariant to affine transforms, so this area seems to be promising...
Are there any segmentation methods more robust to affine (or even projective) deformations of the image?
Answer
MSER (Maximally stable extremal regions) are regions, not points. And they're invariant to affine transformation. But it's not a segmentation method, strictly speaking
Informally speaking, the idea is to find blobs at various thresholds, then select the blobs that have the least change in shape/area over a range of thresholds. These regions should be stable for a large range of grayscale and geometric transformations.
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