This paper deals with the registration of near real-time satellite SAR images by an automatic image-to-image matching approach. The approach is dedicated to crisis mapping applications where the task is to register near real-time (NRT) SAR images within some hours against a reference image. Our

Abstract. Image-to-image registration requires identification of common points in both images (image tie-points; ITPs). Here, we describe software implementing an automated, area-based technique for identifying ITPs. The ITP software was designed to follow two strategies: (1) capitalize on human

Abstract 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

The purpose of this chapter is to present a survey of recent publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods The statistics

Abstract. Image-to-image registration requires identification of common points in both images (irnage tie-points: ITPs). Here. we describe software implementing an automated, area-based technique for identifying ITPs. The ITP software was designed to follow two strategies: ( I) capitalize on human

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

Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton

A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence

of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, we have compared the results of the proposed

georeferenced information. Unlikely, the orbit information is often not very precisely known some hours after data acquisition, which leads to misregistration. This paper deals with the development of a fully automatic orientation for SAR images. The goal is to determine a precise orbit for geocoding by image-to-image