Lisa Gottesfeld Brown
Abstract
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.
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Index Terms
- A survey of image registration techniques
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Reviews
Grigore Albeanu
A taxonomy of two-dimensional image registration techniques is presented based on the types of variations in the images. To fulfill this purpose, the author presents the neces sary terminology and image registration fundamentals in the first two sections, and then describes the registration methods in use (section 3) and their basic characteristics (section 4). The registration methods considered have three types of variations: corrected distortions, uncorrected distortions, and variations of interest. The paper presents correlation and sequential methods in subsection 3.1, Fourier methods in subsection 3.2, methods that use point mapping with and without feedback in subsection 3.3, and methods that exploit the behavior of elastic models in subsection 3.4. Each method is characterized by the complexity of its class of transformations determined by the source of misregistration. Both global and local transformations, variations, and computations are discussed. The material is rich in tables and illustrative figures, and contains a lot of clear explanations. For this complex subject, the length of the paper is just right. The paper contains some misprints, however. For instance, on page 327, a long phrase appears twice, while in the formula for T on page 353, the sign “=” appears in place of “+.” This paper is a good survey of image registration methods. It has a large and valuable collection of references and is useful for a person working in this area.
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