Adaptive Registration of Very Large Images

Brian Jackson & Ardy GoshtasbyWright State University

Characteristics of Very Large Images

• They often have local geometric differences.• Finding correspondences

is nontrivial.•Management of time

and space is a challenge.

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Mapping Function• Break down the overall transformation

into a global and a number of local transformations.• Represent the global transformation by an

affine.• Represent each local transformation by an

affine.• Blend the global and local transformations

into a smooth mapping function.

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Coarse-to-Fine Matching• Reduce resolution sufficiently so

local geometric differences become negligible.• Find global transformation.• Increase resolution and globally

align.• Subdivide image domain, find

local correspondences, and find local transformations.

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Affine Parameters under Scaling

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Combining Global and Local Affines

• Mapping function at a level is obtained from a blending of local affine transformations.• A local affine is obtained from an estimate of the

affine at previous level and a refinement to the estimation.

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Blending Function•Rational Gaussian

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Preliminary Results

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Computational Requirements

• Computationally, it takes a couple of seconds on a Windows PC with Intel i7 processor to register one Mega-pixel images.• It takes 30 seconds to register ten Mega-pixel images.• Computation time increases linearly with image size.• The time to find correspondence between points in

images is about the same as the time to calculate the mapping function and register images.

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Limitations•It cannot register multimodality images.•Nonlinear geometric difference between corresponding blocks is ignored.•Image discontinuities due to occlusion are ignored.

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