http://library.utia.cas.cz/prace/20030125.pdf

Image registration methods: a survey

Registration is a fundamental task in image processing used to match two or more pictures taken, for example, at different times, from different sensors, or from different viewpoints. Virtually all large systems which evaluate images require the registration of images, or a closely related operation, as an intermediate step. Specific examples of systems where image registration is a significant component include matching a target with a real-time image of a scene for target recognition, monitoring global land usage using satellite images, matching stereo images to recover shape for autonomous navigation, and aligning images from different medical modalities for diagnosis.Over the years, a broad range of techniques has been developed for various types of data and problems. These techniques have been independently studied for several different applications, resulting in a large body of research. This paper organizes this material by establishing the relationship between the variations in the images and the type of registration techniques which can most appropriately be applied. Three major types of variations are distinguished. The first type are the variations due to the differences in acquisition which cause the images to be misaligned. To register images, a spatial transformation is found which will remove these variations. The class of transformations which must be searched to find the optimal transformation is determined by knowledge about the variations of this type. The transformation class in turn influences the general technique that should be taken. The second type of variations are those which are also due to differences in acquisition, but cannot be modeled easily such as lighting and atmospheric conditions. This type usually effects intensity values, but they may also be spatial, such as perspective distortions. The third type of variations are differences in the images that are of interest such as object movements, growths, or other scene changes. Variations of the second and third type are not directly removed by registration, but they make registration more difficult since an exact match is no longer possible. In particular, it is critical that variations of the third type are not removed. Knowledge about the characteristics of each type of variation effect the choice of feature space, similarity measure, search space, and search strategy which will make up the final technique. All registration techniques can be viewed as different combinations of these choices. This framework is useful for understanding the merits and relationships between the wide variety of existing techniques and for assisting in the selection of the most suitable technique for a specific problem.

A survey of image registration techniques

Describes an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eye care specialists for purposes of patient screening, treatment evaluation, and clinical study. The authors' method differs from previously known methods in that it uses local and global vessel features cooperatively to segment the vessel network. The authors evaluate their method using hand-labeled ground truth segmentations of 20 images. A plot of the operating characteristic shows that the authors' method reduces false positives by as much as 15 times over basic thresholding of a matched filter response (MFR), at up to a 75% true positive rate. For a baseline, they also compared the ground truth against a second hand-labeling, yielding a 90% true positive and a 4% false positive detection rate, on average. These numbers suggest there is still room for a 15% true positive rate improvement, with the same false positive rate, over the authors' method. They are making all their images and hand labelings publicly available for interested researchers to use in evaluating related methods.

/pdf/locating-blood-vessels-in-retinal-images-by-piecewise-4lgaualy1f.pdf

Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

https://www.cs.princeton.edu/courses/archive/spr05/cos598E/bib/rui99_cbir_survey.pdf

Image Retrieval

A distance transformation converts a binary digital image, consisting of feature and non-feature pixels, into an image where all non-feature pixels have a value corresponding to the distance to the nearest feature pixel. Computing these distances is in principle a global operation. However, global operations are prohibitively costly. Therefore algorithms that consider only small neighborhoods, but still give a reasonable approximation of the Euclidean distance, are necessary. In the first part of this paper optimal distance transformations are developed. Local neighborhoods of sizes up to 7×7 pixels are used. First real-valued distance transformations are considered, and then the best integer approximations of them are computed. A new distance transformation is presented, that is easily computed and has a maximal error of about 2%. In the second part of the paper six different distance transformations, both old and new, are used for a few different applications. These applications show both that the choice of distance transformation is important, and that any of the six transformations may be the right choice.

https://studentportalen.uu.se/uusp-filearea-tool/download.action?nodeId=214320&toolAttachmentId=64777

Distance transformations in digital images

Parametric correspondence is a technique for matching images to a three dimensional symbolic reference map. An analytic camera model is used to predict the location and appearance of landmarks in the image, generating a projection for an assumed viewpoint. Correspondence is achieved by adjusting the parameters of the camera model until the appearances of the landmarks optimally match a symbolic description extracted from the image.

The matching of image and map features is performed rapidly by a new technique, called "chamfer matching", that compares the shapes of two collections of shape fragments, at a cost proportional to linear dimension, rather than area. These two techniques permit the matching of spatially extensive features on the basis of shape, which reduces the risk of ambiguous matches and the dependence on viewing conditions inherent in conventional image based correlation matching.

/pdf/parametric-correspondence-and-chamfer-matching-two-new-1y9p61iemw.pdf

Parametric correspondence and chamfer matching: two new techniques for image matching

Detection of roads and linear structures in low-resolution aerial imagery using a multisource knowledge integration technique

Linear delineation

Introduction A central scientific goal of image understanding research is to investigate ways of bringing diverse sources of knowledge to bear on the problem of interpreting images. An important source of knowledge used by humans in the interpretation of aerial photographs is the topographic map. This paper describes some initial experiments in the use of map knowledge to automate an important class of photo-interpretation tasks involving monitoring and change detection. This work is part of a larger image understanding research program aimed at developing collaborative aids for photo interpreters and cartographers [Barrow 1977a]. Photo interpreters spend a significant proportion of their time in routine monitoring tasks such as counting box cars stored in railyards or keeping track of the ships berthed in a harbor. A major difficulty in automating such tasks is locating the monitoring sites in sensed imagery, which may be taken from arbitrary viewpoints. Once the location is known many monitoring tasks are reduced to simpler detection problems that can be solved by straightforward techniques such as template matching.

/pdf/experiments-in-map-guided-photo-interpretation-1zmisl7dqt.pdf

Experiments in map-guided photo interpretation

: In this paper, the authors address a basic problem in machine perception: the tracing of "line-like" structures appearing in an image. It is shown that this problem can be viewed as the process of finding skeletons in a gray-scale image after observing the following: (1) that line detection does not necessarily depend on gradient information, but rather is approachable from the standpoint of measuring total intensity variation; and (2) that smoothing the original image produces an approximate distance transform. An effective technique for extracting the delineating skeletons from an image is presented, and examples of this approach using aerial, industrial, and radiographic imagery are shown.

H. C. Wolf

Papers

Parametric correspondence and chamfer matching: two new techniques for image matching

Detection of roads and linear structures in low-resolution aerial imagery using a multisource knowledge integration technique

Linear delineation

Experiments in map-guided photo interpretation

A General Approach to Machine Perception of Linear Structure in Imaged Data