View and access the latest DualAlign papers below.
The Dual-Bootstrap Iterative Closest Point algorithm with application to retinal image registration
Stewart, C.V.; Chia - Ling Tsai; Roysam, B.
Medical Imaging, IEEE Transactions on
Volume 22, Issue 11, Nov. 2003 Page(s): 1379 - 1394
Abstract
Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called Dual-Bootstrap Iterative Closest Point (Dual-Bootstrap ICP). The approach is to start from one or more initial, low-order estimates that are only accurate in small image regions, called bootstrap regions. In each bootstrap region, the algorithm iteratively: 1) refines the transformation estimate using constraints only from within the bootstrap region; 2) expands the bootstrap region; and 3) tests to see if a higher order transformation model can be used, stopping when the region expands to cover the overlap between images. Steps 1): and 3), the bootstrap steps, are governed by the covariance matrix of the estimated transformation. Estimation refinement Step 2) uses a novel robust version of the ICP algorithm. In registering retinal image pairs, Dual-Bootstrap ICP is initialized by automatically matching individual vascular landmarks, and it aligns images based on detected blood vessel centerlines. The resulting quadratic transformations are accurate to less than a pixel. On tests involving approximately 6000 image pairs, it successfully registered 99.5% of the pairs containing at least one common landmark, and 100% of the pairs containing at least one common landmark and at least 35% image overlap.
The Generalized Dual-Bootstrap ICP algorithm with application to registering challenging image pairs
G. Yang, C.V. Stewart, M. Sofka, and C.-L. Tsai,
Machine Intelligence, IEEE Transactions on Pattern Analysis
accepted for publication, 2007
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% of the misalignments that occur when all possible pairs are tried. The algorithm substantially out-performs algorithms based on keypoint matching alone.
Estimating the location of a camera with respect to a 3d model
Proceedings of the IEEE International
G. Yang, J. Becker, C.V. Stewart,
Conference on Recent Advances in 3-D Digital Imaging and Modeling
(3DIM), 2007.
Abstract
An algorithm is presented to estimate the position of a hand-held camera with respect to a 3d world model constructed from range data and color imagery. Little prior knowledge is assumed about the camera position. The algorithm includes stages that (1) generate an ordered set of initial model-to-image mapping estimates, each accurate only in a small region of the image and of the model, (2) refinement of each initial estimate through a combination of 3d-to-2d matching, robust parameter estimation, region growth, and model selection, and (3) testing the resulting projections for accuracy, stability and randomness. A key issue during stage (2) is that initially the model-to-image mapping is well-approximated by a 2d-to-2d transformation based on a local model surface approximation, but eventually the algorithm must transition to the 3d-to-2d projection necessary to solve the position estimation problem. The algorithm accomplishes this by expanding the region along the approximation surface first and then making a transition to expand fully in 3d. The overall algorithm is shown to effectively determine the location of the camera over a 100m x 100m area of our campus.
