This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (areabased and feature-based) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image transformation and resampling. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of image registration and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas. q 2003 Elsevier B.V. All rights reserved.

We present an automatic sub-pixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images (2-D) or volumes (3-D). It uses an explicit spline representation of the images in conjunction with spline processing, and is based on a coarse-to-fine iterative strategy (pyramid approach). The minimization is performed according to a new variation (ML*) of the Marquardt-Levenberg algorithm for non-linear least-square optimization. The geometric deformation model is a global 3-D affine transformation that can be optionally restricted to rigid-body motion (rotation and translation), combined with isometric scaling. It also includes an optional adjustment of image contrast differences. We obtain excellent results for the registration of intra-modality Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI) data. We conclude that the multi-resolution refinement strategy is more robust than a comparable single-stage method, being less likely to be trapped into a false local optimum. In addition, our improved version of the Marquardt-Levenberg algorithm is faster.

We present direct featureless methods for estimating the eight parameters of an "exact" projective (homographic) coordinate transformation to register pairs of images, together with the application of seamlessly combining a plurality of images of the same scene, resulting in a single image (or new image sequence) of greater resolution or spatial extent. The approach is "exact" for two cases of static scenes: 1) images taken from the same location of an arbitrary three-dimensional (3-D) scene, with a camera that is free to pan, tilt, rotate about its optical axis, and zoom, or 2) images of a flat scene taken from arbitrary locations. The featureless projective approach generalizes interframe camera motion estimation methods that have previously used an affine model (which lacks the degrees of freedom to "exactly" characterize such phenomena as camera pan and tilt) and/or which have relied upon finding points of correspondence between the image frames. The featureless projective approach...

It is straightforward to apply general schemes for authenticating digital data to the problem of authenticating digital images. However, such a scheme would not authenticate images that have undergone lossy compression, even though they may not have been manipulated otherwise. In this paper we propose a scheme for authenticating the visual content of digital images. This scheme is robust to compression noise, but will detect deliberate manipulation of the image-data. The proposed scheme is based on the extraction of featurepoints from the image. These feature-points are defined so as to be relatively unaffected by lossy compression. The set of feature-points from a given image is encrypted using public key encryption, to generate the digital signature of the image. Authenticity is verified by comparing the feature-points of the image in question, with those recovered from the previously computed digital signature. 1 Introduction Verification of data integrity has become a very import...

Image registration is concerned with the establishment of correspondence between images of the same scene. One challenging problem in this area is the registration of multispectral/multisensor images. In general, such images have different gray level characteristics, and simple techniques such as those based on area correlations cannot be applied directly. On the other hand, contours representing region boundaries are preserved in most cases. In this paper, we present two contour-based methods which use region boundaries and other strong edges as matching primitives. The first contour matching algorithm is based on the chain.code correlation and other shape similarity criteria such as invariant moments. Closed contours and the salient segments along the open contours are matched separately. This method works well for image pairs in which the contour information is well preserved, such as the optical images from Landsat and Spot satellites. For the registration of the optical images with synthetic aperture radar (SAR) images, we propose an elastic contour matching scheme based on the active contour model. Using the contours from the optical image as the initial condition, accurate contour locations in the SAR image are obtained by applying the active contour model. Both contour matching methods are automatic and computationally quite efficient. Experimental results with various kinds of image data have verified the robustness of our algorithms, which have outperformed manual registration in terms of root mean square error at the control points.

Abstract—This paper presents a new direct Fourier-based algorithm for performing image-to-image registration to subpixel accuracy, where the image differences are restricted to translations and uniform changes of illumination. The algorithm detects the Fourier components that have become unreliable estimators of shift due to aliasing, and removes them from the shift-estimate computation. In the presence of aliasing, the average precision of the registration is a few hundredths of a pixel. Experimental data presented here show that the new algorithm yields superior registration precision in the presence of aliasing when compared to several earlier methods and has comparable precision to the iterative method of Thévenaz et al. [21]. Index Terms—Aliasing, Fourier transform, image registration, spline interpolation, subpixel.

We present a new technique for estimating the projective (homographic) coordinate transformation between pairs of images, taken with a camera that is free to pan, tilt, rotate about its optical axis, and zoom. The technique solves the problem for two cases of static scenes: images taken from the same location of an arbitrary 3-D scene, or images taken from arbitrary locations of a flat scene. A new algorithm is presented for the parameter estimation and applied to the task of constructing high resolution still images from video. This approach generalizes inter-frame camera motion estimation methods which have previously used an affine model and/or which have relied upon finding points of correspondence between the image frames. The new projective algorithm which operates directly on the image pixels is shown to be superior in accuracy and ability to enhance resolution. The proposed method works well on image data collected from both good-quality and poor-quality video under a wide vari...

Although laser retinal surgery is the best available treatment for choridal neovascularization, the current procedure has a low success rate (50%). Challenges, such as motioncompensated beam steering, ensuring complete coverage and minimizing incidental photodamage, can be overcome with improved instrumentation. This paper presents core image processing algorithms for 1) rapid identification of branching and crossover points of the retinal vasculature; 2) automatic montaging of video retinal angiograms; 3) real-time location determination and tracking using a combination of feature-tagged point-matching and dynamic-pixel templates. These algorithms tradeoff conflicting needs for accuracy, robustness to image variations (due to movements and the difficulty of providing steady illumination) and noise, and operational speed in the context of available hardware. The algorithm for locating vasculature landmarks performed robustly at a speed of 16--30 video image frames/s depending upon the field on a Silicon Graphics workstation. The montaging algorithm performed at a speed of 1.6--4 s for merging 5--12 frames. The tracking algorithm was validated by manually locating six landmark points on an image sequence with 180 frames, demonstrating a mean-squared error of 1.35 pixels. It successfully detected and rejected instances when the image dimmed, faded, lost contrast, or lost focus.

Invariant representations are frequently used in computer vision algorithms to eliminate the effect of an unknown transformation of the data. These representations, however, depend on the order in which the features are considered in the computations. We introduce the class of projective/permutation p -invariants which are insensitive to the labeling of the feature set. A general method to compute the p -invariant of a point set (or of its dual) in the n-dimensional projective space is given. The one-to-one mapping between n 3 points and the components of their p -invariant representation makes it possible to design correspondence algorithms with superior tolerance to positional errors. An algorithm for coplanar points in projective correspondence is described as an application, and its performance is investigated. The use of p -invariants as an indexing tool in object recognition systems may also be of interest.

A new practical, high-performance mobile robot localization technique is described which is motivated by the fact that many man-made environments contain substantially flat, visually textured surfaces of persistent appearance.