Registration of Challenging Image Pairs: Initialization, Estimation, and Decision

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Registration of Challenging Image Pairs: Initialization, Estimation, and Decision (2007)

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by Gehua Yang , Charles V. Stewart , Michal Sofka , Chia-Ling Tsai

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@MISC{Yang07registrationof,
    author = {Gehua Yang and Charles V. Stewart and Michal Sofka and Chia-Ling Tsai},
    title = {Registration of Challenging Image Pairs: Initialization, Estimation, and Decision},
    year = {2007}
}

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Abstract

Our goal is an automated 2D-image-pair registration algorithm capable of aligning images taken of a wide variety of natural and man-made scenes as well as many medical images. The algorithm should handle low overlap, substantial orientation and scale differences, large illumination variations, and physical changes in the scene. An important component of this is the ability to automatically reject pairs that have no overlap or have too many differences to be aligned well. We propose a complete algorithm including techniques for initialization, for estimating transformation parameters, and for automatically deciding if an estimate is correct. Keypoints extracted and matched between images are used to generate initial similarity transform estimates, each accurate over a small region. These initial estimates are rank-ordered and tested individually in succession. Each estimate is refined using the Dual-Bootstrap ICP algorithm, driven by matching of multiscale features. A three-part decision criteria, combining measurements of alignment accuracy, stability in the estimate, and consistency in the constraints, determines whether the refined transformation estimate is accepted as correct. Experimental results on a data set of 22 challenging image pairs show that the algorithm effectively aligns 19 of the 22 pairs and rejects 99.8 percent of the misalignments that occur when all possible pairs are tried. The algorithm substantially out-performs algorithms based on keypoint matching alone.

Keyphrases

challenging image pair complete algorithm multiscale feature dual-bootstrap icp algorithm initial estimate alignment accuracy substantial orientation large illumination variation image pair man-made scene refined transformation estimate possible pair automated 2d-image-pair registration algorithm scale difference three-part decision criterion important component small region initial similarity transform estimate out-performs algorithm physical change low overlap many difference data set wide variety transformation parameter many medical image experimental result

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