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....

This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.

By panning a camera over a scene and automatically compositing the video frames, this system creates large panoramic images of arbitrary shape and detail. Depth recovery from motion parallax also enables limited 3D rendering. The use of photographic imagery as part of the computer graphics creation process is a well established and popular technique. Still imagery can be used in a variety of ways, including the manipulation and compositing of photographs inside video paint systems, and the texture mapping of still photographs onto 3D graphical models to achieve photorealism. Although laborious, it is also possible to merge 3D computer graphics seamlessly with video imagery to produce dramatic special effects. As computer-based video becomes ubiquitous with the expansion of transmission, storage, and manipulation capabilities, it will offer a rich source of imagery for computer graphics applications. This article looks at one way to use video as a new source of high-resolution, photorealistic imagery for these applications. In its current broadcast-standard forms, video is a low-resolution medium that compares poorly with computer displays and scanned imagery. It also suffers, as do all input imaging devices, from a limited field of view.

A pair of rendered images and their Z-buffers contain almost all of the information necessary to re-render from nearby viewpoints. For the small changes in viewpoint that occur in a fraction of a second, this information is sufficient for high quality re-rendering with cost independent of scene complexity. Re-rendering from previously computed views allows an order-of-magnitude increase in apparent frame rate over that provided by conventional rendering alone. It can also compensate for system latency in local or remote display. We use

Abstract—The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and object recognition. While the traditional approach relies on the use of 3D models, we have recently introduced [1], [2], [3] simpler techniques that are applicable under restricted conditions. The approach exploits image transformations that are specific to the relevant object class, and learnable from example views of other “prototypical ” objects of the same class. In this paper, we introduce such a technique by extending the notion of linear class proposed by Poggio and Vetter. For linear object classes, it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively “rotate ” highresolution face images from a single 2D view. Index Terms—3D object recognition, rotation invariance, deformable templates, image synthesis. 1

Modal matching is a new method for establishing correspondences and computing canonical descriptions. The method is based on the idea of describing objects in terms of generalized symmetries, as defined by each object's eigenmodes. The resulting modal description is used for object recognition and categorization, where shape similarities are expressed as the amounts of modal deformation energy needed to align the two objects. In general, modes provide a global-to-local ordering of shape deformation and thus allow for selecting which types of deformations are used in object alignment and comparison. In contrast to previous techniques, which required correspondence to be computed with an initial or prototype shape, modal matching utilizes a new type of finite element formulation that allows for an object's eigenmodes to be computed directly from available image information. This improved formulation provides greater generality and accuracy, and is applicable to data of any dimensionality. Correspondence results with 2-D contour and point feature data are shown, and recognition experiments with 2-D images of hand tools and airplanes are described.

While a large number of virtual reality applications, such as fluid flow analysis and molecular modeling, deal with simulated data, many newer applications attempt to recreate true reality as convincingly as possible. Building detailed models for such applications, which we call tele-reality, is a major bottleneck holding back their deployment. In this paper, we present techniques for automatically deriving realistic 2-D scenes and 3-D texture-mapped models from video sequences, which can help overcome this bottleneck. The fundamental technique we use is image mosaicing, i.e., the automatic alignment of multiple images into larger aggregates which are then used to represent portions of a 3-D scene. We begin with the easiest problems, those of flat scene and panoramic scene mosaicing, and progress to more complicated scenes, culminating in full 3-D models. We also present a number of novel applications based on tele-reality technology.

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In this paper, we survey the techniques for image-based rendering. Unlike traditional 3D computer graphics in which 3D geometry of the scene is known, image-based rendering techniques render novel views directly from input images. Previous image-based rendering techniques can be classified into three categories according to how much geometric information is used: rendering without geometry, rendering with implicit geometry (i.e., correspondence), and rendering with explicit geometry (either with approximate or accurate geometry). We discuss the characteristics of these categories and their representative methods. The continuum between images and geometry used in image-based rendering techniques suggests that image-based rendering with traditional 3D graphics can be united in a joint image and geometry space. Keywords: Image-based rendering, survey. 1

Abstract — Printing from an NTSC source and conversion of NTSC source material to high-definition television (HDTV) format are some of the recent applications that motivate superresolution (SR) image and video reconstruction from lowresolution (LR) and possibly blurred sources. Existing methods for SR image reconstruction are limited by the assumptions that the input LR images are sampled progressively, and that the aperture time of the camera is zero, thus ignoring the motion blur occurring during the aperture time. Because of the observed adverse effects of these assumptions for many common video sources, this paper proposes i) a complete model of video acquisition with an arbitrary input sampling lattice and a nonzero aperture time, and ii) an algorithm based on this model using the theory of projections onto convex sets to reconstruct SR still images or video from an LR time sequence of images. Experimental results with real video are provided, which clearly demonstrate that a significant increase in the image resolution can be achieved by taking the motion blurring into account especially when there exists large interframe motion. Index Terms — Superresolution, video stills, video resampling, standards conversion. I.