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. Over the years, a broad range of techniques have been developed for the 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 distortions in the image and the type of registration techniques which are most suitable. Two major types of distortions are distinguished. The first type are those which are the source of misregistration, i.e., they are the cause of the misalignment between the two images. Distortions which are the source of misregistration determine the transformation class which will optimally align the two images. The transformation class in turn influences the general technique that should be taken....

In this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this

Introduction The ever expanding gamut of medical imaging techniques provides the clinician an increasingly multifaceted view of brain function and anatomy. The information provided by the various imaging modalities is often complementary (i.e. provides separate but useful information) and synergistic (i.e. the combination of information provides useful extra information). For example, X-ray computed tomography (CT) and magnetic resonance (MR) imaging exquisitely demonstrate brain anatomy but provide little functional information. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) scans display aspects of brain function and allow metabolic measurements but poorly delineate anatomy. Furthermore, CT and MR images describe complementary morphologic features. For example, bone and calcifications are best seen on CT images, while soft-tissue structures are better differentiated by MR imaging. Clinical diagnosis and therapy planning and evaluatio

Abstract-Two methods of measuring ocular torsion from digital images of the eyes were developed and tested. One method measures torsion from the translation of two landmarks using a rectilinear coordinate system. The second method measures torsion from the translation of two landmarks using a polar coordinate system. The center for the polar sampling is the center of the pupil. After thresholding and filtering the images, landmark translation is measured from the interpolated peak in the normalized cross correlation of the reference landmark with the image. The standard deviation of the measurement error for the first method using artificially rotated well-framed 256 X 256 X 8 single-eye images was 0.0420 in the absence of noise and 0.0610 for a noise-to-signal ratio of 0.1. The corresponding measurement accuracies for the radial sampling method were 0.0190 and 0.0310. The precision of the torsion measurement for high-quality experimental images was 0.1320. The landmark tracking method on the rectilinear grid can be used when the rotation is within a ±50 range. The measurement technique using the polar sampling can be used when there is a single point which is moderately well known. Thus, digital signal processing techniques can be used to measure ocular torsion from images of the eye with a precision similar to the precision obtained by human photographic interpretation. The precision of the measurement does not appear to be limited by the precision of the digital processing technique.

put of the detector plane of (c) is shown in (e). The entire surface is smoother than (d). The uncorrupted corner and the blurred feature give a less pronounced peak; the position of the corrupted corner cannot be detected with confidence and several likely locations are indicated by the smooth hill. Thus, detection and placement can be improved by using sharp feature representations. The aim of this chapter is to develop feature sets with sharp contours. Three amendments to the previously proposed architecture are proposed: the use of spatial competition during training is outlined in x6.2, the selection of a subset of features from a larger set is suggested in x6.3, and the application of threshold-like, feature post-processing is discussed in x6.4. First a description of the three methods is given which is followed by an experimental investigation in x6.5. The new feature types of the three methods are given in

editorsÕ introduction Image registration is the process of determining correspondence between all points in two or more images of the same scene. Image analysis applications that involve multiple images of a scene often require registration of the images. Nonrigid image registration refers to a class of methods where the images to be registered have geometric differences that cannot be accounted for by similarity (global translation, rotation, and scaling) transformations. Image registration has a long history. One of the first examples of image registration appeared in the work of Roberts [33]. By aligning projections of edges of polyhedral solids with image edges, he was able to locate and recognize predefined polyhedral objects in images. Registration of entire images first appeared in the remote sensing literature. Anuta [1,2] and Barnea and Silverman [8] developed automatic methods for registering satellite images using the sum of absolute differences as the similarity measure. Leese et al. [20] and Pratt [31] did the same using cross-correlation coefficient as the similarity measure. Use of image registration in the computation

Abstract not found