Charles V. Stewart

The dual-bootstrap iterative closest point algorithm with application to retinal image registration

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.

Multiple kernel tracking with SSD

Kernel-based objective functions optimized using the mean shift algorithm have been demonstrated as an effective means of tracking in video sequences. The resulting algorithms combine the robustness and invariance properties afforded by traditional density-based measures of image similarity, while connecting these techniques to continuous optimization algorithms. This paper demonstrates a connection between kernel-based algorithms and more traditional template tracking methods. here is a well known equivalence between the kernel-based objective function and an SSD-like measure on kernel-modulated histograms. It is shown that under suitable conditions, the SSD-like measure can be optimized using Newton-style iterations. This method of optimization is more efficient (requires fewer steps to converge) than mean shift and makes fewer assumptions on the form of the underlying kernel structure. In addition, the methods naturally extend to objective functions optimizing more elaborate parametric motion models based on multiple spatially distributed kernels. We demonstrate multi-kernel methods on a variety of examples ranging from tracking of unstructured objects in image sequences to stereo tracking of structured objects to compute full 3D spatial location.

A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina

This paper describes a robust hierarchical algorithm for fully-automatic registration of a pair of images of the curved human retina photographed by a fundus microscope. Accurate registration is essential for mosaic synthesis, change detection, and design of computer-aided instrumentation. Central to the algorithm is a 12-parameter interimage transformation derived by modeling the retina as a rigid quadratic surface with unknown parameters. The parameters are estimated by matching vascular landmarks by recursively tracing the blood vessel structure. The parameter estimation technique, which could be generalized to other applications, is a hierarchy of models and methods, making the algorithm robust to unmatchable image features and mismatches between features caused by large interframe motions. Experiments involving 3,000 image pairs from 16 different healthy eyes were performed. Final registration errors less than a pixel are routinely achieved. The speed, accuracy, and ability to handle small overlaps compare favorably with retinal image registration techniques published in the literature.

MINPRAN: a new robust estimator for computer vision

MINPRAN is a new robust estimator capable of finding good fits in data sets containing more than 50% outliers. Unlike other techniques that handle large outlier percentages, MINPRAN does not rely on a known error bound for the good data. Instead, it assumes the bad data are randomly distributed within the dynamic range of the sensor. Based on this, MINPRAN uses random sampling to search for the fit and the inliers to the fit that are least likely to have occurred randomly. It runs in time O(N/sup 2/+SN log N), where S is the number of random samples and N is the number of data points. We demonstrate analytically that MINPRAN distinguished good fits to random data and MINPRAN finds accurate fits and nearly the correct number of inliers, regardless of the percentage of true inliers. We confirm MINPRANs properties experimentally on synthetic data and show it compares favorably to least median of squares. Finally, we apply MINPRAN to fitting planar surface patches and eliminating outliers in range data taken from complicated scenes. >

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

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.

Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images

A model-based algorithm, termed exclusion region and position refinement (ERPR), is presented for improving the accuracy and repeatability of estimating the locations where vascular structures branch and cross over, in the context of human retinal images. The goal is two fold. First, accurate morphometry of branching and crossover points (landmarks) in neuronal/vascular structure is important to several areas of biology and medicine. Second, these points are valuable as landmarks for image registration, so improved accuracy and repeatability in estimating their locations and signatures leads to more reliable image registration for applications such as change detection and mosaicing. The ERPR algorithm is shown to reduce the median location error from 2.04 pixels down to 1.1 pixels, while improving the median spread (a measure of repeatability) from 2.09 pixels down to 1.05 pixels. Errors in estimating vessel orientations were similarly reduced from 7.2/spl deg/ down to 3.8/spl deg/.

Robust Computer Vision

This special issue is dedicated to examining the use of techniques from robust statistics in solving computer vision problems. It represents a milestone of recent progress within a subarea of our field that is nearly as old as the field itself, but has seen rapid growth over the past decade. Our Introduction considers the meaning of robustness in computer vision, summarizes the papers, and outlines the relationship between techniques in computer vision and statistics as a means of highlighting future directions. It complements the available reviews on this topic [12, 13].

MUSE: robust surface fitting using unbiased scale estimates

Despite many successful applications of robust statistics, they have yet to be completely adapted to many computer vision problems. Range reconstruction, particularly in unstructured environments, requires a robust estimator that not only tolerates a large outlier percentage but also tolerates several discontinuities, extracting multiple surfaces in an image region. Observing that random outliers and/or points from across discontinuities increase a hypothesized fits scale estimate (standard deviation of the noise), our new operator; called MUSE (Minimum Unbiased Scale Estimator), evaluates a hypothesized fit over potential inlier sets via an objective function of unbiased scale estimates. MUSE extracts the single best fit from the data by minimizing its objective function over a set of hypothesized fits and can sequentially extract multiple surfaces from an image region. We show MUSE to be effective on synthetic data modelling small scale discontinuities and in preliminary experiments on complicated range data.

Robust hierarchical algorithm for constructing a mosaic from images of the curved human retina

This paper describes computer vision algorithms to assist in retinal laser surgery, which is widely used to treat leading blindness causing conditions but only has a 50% success rate, mostly due to a lack of spatial mapping and reckoning capabilities in current instruments. The novel technique described here automatically constructs a composite (mosaic) image of the retina from a sequence of incomplete views. This mosaic will be useful to ophthalmologists for both diagnosis and surgery. The new technique goes beyond published methods in both the medical and computer vision literatures because it is fully automated, models the patient-dependent curvature of the retina, handles large interframe motions, and does not require calibration. At the heart of the technique is a 12-parameter image transformation model derived by modeling the retina as a quadratic surface and assuming a weak perspective camera, and rigid motion. Estimating the parameters of this transformation model requires robustness to unmatchable image features and mismatches between features caused by large interframe motions. The described estimation technique is a hierarchy of models and methods: the initial match set is pruned based on a 0th order transformation estimated using a similarity-weighted histogram; a 1st order affine transformation is estimated using the reduced match set and least-median of squares; and the final, 2nd order 12-parameter transformation is estimated using an M-estimator initialized from the 1st order results. Initial experimental results show the method to be robust and accurate in accounting for the unknown retinal curvature in a fully automatic manner while preserving image details.

Robust focus ranging

Depth maps obtained from focus ranging can have numerous errors and distortions due to edge bleeding, feature shifts, image noise, and field curvature. An improved algorithm that examines an initial high depth-of-field image of the scene to identify regions susceptible to edge bleeding and image noise is given. Focus evaluation windows are adapted to local image content and optimize the tradeoff between spatial resolution and noise sensitivity. An elliptical paraboloid field curvature model is used to reduce range distortion in peripheral image areas. Spatio-temporal tracking compensates for image feature shifts. The result is a sparse but reliable depth map.<<ETX>>