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Pairwise alignment, image stitching, global alignment, and compositing methods.
Feature matching tells us which points correspond—but now what? We need to compute the actual transformation that aligns one image to another. This chapter covers the geometry and algorithms for image alignment.
What is this chapter about? We learn to compute geometric transformations from matched features, handle the inevitable mismatches with RANSAC, and combine multiple images into seamless panoramas.
Why does this matter? Image alignment enables:
How the topics connect: We start with homographies—the transformation model for planar scenes. Then RANSAC handles outliers in our feature matches. Panorama stitching puts it all together, including blending to hide seams.
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Projective transformations, DLT, the transformation hierarchy — the geometric model for planar alignment.
Robust estimation under outliers — random sampling, iteration bounds, adaptive termination.
Two routes to multi-image alignment
Full stitching pipeline — warping, multi-band blending, and exposure compensation for seamless mosaics.
Global optimization of cameras and structure — Levenberg-Marquardt, Schur complement, robust costs.
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