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Image registration methods: a survey
 IMAGE AND VISION COMPUTING
, 2003
"... 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 geometrically align t ..."
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Cited by 621 (8 self)
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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 geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (areabased and featurebased) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image transformation and resampling. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of image registration and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas.
Regularization Theory and Neural Networks Architectures
 Neural Computation
, 1995
"... We had previously shown that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called Regularization Networks. In particular, standard smoothness functionals lead to a subclass of regularization networks, the well known Radial Ba ..."
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Cited by 374 (31 self)
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We had previously shown that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called Regularization Networks. In particular, standard smoothness functionals lead to a subclass of regularization networks, the well known Radial Basis Functions approximation schemes. This paper shows that regularization networks encompass a much broader range of approximation schemes, including many of the popular general additive models and some of the neural networks. In particular, we introduce new classes of smoothness functionals that lead to different classes of basis functions. Additive splines as well as some tensor product splines can be obtained from appropriate classes of smoothness functionals. Furthermore, the same generalization that extends Radial Basis Functions (RBF) to Hyper Basis Functions (HBF) also leads from additive models to ridge approximation models, containing as special cases Breiman's hinge functions, som...
ModelBased Recognition and Localization From Sparse Range or Tactile Data
, 1983
"... This paper discusses how local measurements of threedimensional pool[ions and surface normals (recorded by a set of tactile sensors, or by threedimensional range sensors), may be used o identify and locate objects, from among a set, of known objects. The objects are modeled as po!yhedra having up t ..."
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Cited by 160 (7 self)
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This paper discusses how local measurements of threedimensional pool[ions and surface normals (recorded by a set of tactile sensors, or by threedimensional range sensors), may be used o identify and locate objects, from among a set, of known objects. The objects are modeled as po!yhedra having up to six degrees of freedom relative to the sensors. We show tiat inconsistent, hypotheses about pairings between sensed points and object, surfaces can be discarded efficiently by using local constraints on: distoances bet,ween faces, angles betwee, face normals, and angles (reiatAve to t. he surface normals) of vectors between sensed points. We show by simulation and by mathematical bounds that the number of hypotheses consisten; with these constraints is small. We also show how to recover the position and orient, at, ion of the object from the sense daiwa. The algorithm's performance on data obt,ained from a triangulation range sensor is illustrated.
Single Lens Stereo with a Plenoptic Camera
, 1992
"... Ordinary cameras gather light across the area of their lens aperture, and the light striking a given subregion of the aperture is structured somewhat differently than the light striking an adjacent subregion. By analyzing this optical structure, one can infer the depths of objects in the scene, i.e. ..."
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Cited by 152 (0 self)
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Ordinary cameras gather light across the area of their lens aperture, and the light striking a given subregion of the aperture is structured somewhat differently than the light striking an adjacent subregion. By analyzing this optical structure, one can infer the depths of objects in the scene, i.e., one can achieve "single lens stereo." We describe a novel camera for performing this analysis. It incorporates a single main lens along with a lenticular array placed at the sensor plane. The resulting "plenoptic camera" provides information about how the scene would look when viewed from a continuum of possible viewpoints bounded by the main lens aperture. Deriving depth information is simpler than in a binocular stereo system because the correspondence problem is minimized. The camera extracts information about both horizontal and vertical parallax, which improves the reliability of the depth estimates.
Selection of a convolution function for Fourier inversion using gridding [computerised tomography application
 IEEE Trans. Medical Imaging
, 1991
"... AbstractIn fields ranging from radio astronomy to magnetic resonance imaging, Fourier inversion of data not falling on a Cartesian grid has been a prbblem. As a result, multiple algorithms have been created for reconstructing images from nonuniform frequency samples. In the technique known as gridd ..."
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Cited by 123 (1 self)
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AbstractIn fields ranging from radio astronomy to magnetic resonance imaging, Fourier inversion of data not falling on a Cartesian grid has been a prbblem. As a result, multiple algorithms have been created for reconstructing images from nonuniform frequency samples. In the technique known as gridding, the data samples are weighted for sampling density and convolved with a finite kernel, then resampled on a grid preparatory to a fast Fourier transform. This paper compares the utility of several convolution functions, including one that outperforms the “optimal ” prolate spheroidal wave function in some situations. I.
Computational Experiments with a Feature Based Stereo Algorithm
, 1984
"... Computational models of the human stereo system' can provide insight into general information processing constraints that apply to any stereo system, either artificial or biological. In 1977, Marr and Poggio proposed one such computational model, that was characterized as matching certain featu ..."
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Cited by 103 (0 self)
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Computational models of the human stereo system' can provide insight into general information processing constraints that apply to any stereo system, either artificial or biological. In 1977, Marr and Poggio proposed one such computational model, that was characterized as matching certain feature points in differenceofGaussian filtered images, and using the information obtained by matching coarser resolution representations to restrict the search'space for matching finer resolution representations. An implementation of the algorithm and'its testing on a range of images was reported in 1980. Since then a number of psychophysical experiments have suggested possible refinements to the model and modifications to the algorithm. As well, recent computational experiments applying the algorithm to a variety of natural images, especially aerial photographs, have led to a number of modifications. In this article, we present a version of the MarrPoggioGfimson algorithm that embodies these modifications and illustrate its performance on a series of natural images.
Signal Matching Through Scale Space
 International Journal of Computer Vision
, 1987
"... Given a collection of similar signals that have been deformed with respect to each other, the general signalmatching problem is to recover the deformation. We formulate the problem as the minimization of an energy measure that combines a smoothness term and a similarity term. The minimization reduc ..."
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Cited by 83 (3 self)
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Given a collection of similar signals that have been deformed with respect to each other, the general signalmatching problem is to recover the deformation. We formulate the problem as the minimization of an energy measure that combines a smoothness term and a similarity term. The minimization reduces to a dynamic system governed by a set of coupled, firstorder differential equations. The dynamic system finds an optimal solution at a coarse scale and then tracks it continuously to a fine scale. Among the major themes in recent work on visual signal matching have been the notions of matching as constrained optimization, of variational surface reconstruction, and of coarsetofine matching. Our solution captures these in a precise, succinct, and unified form. Results are presented for onedimensional signals, a motion sequence, and a stereo pair. 1
The Direct Computation of Height from Shading
 In Conference on Computer Vision and Pattern Recognition
, 1991
"... We present a method of recovering shape from shading that solves directly for the surface height. By using a discrete formulation of the problem, we are able to achieve good convergence behavior by employing numerical solution techniques more powerful than gradient descent methods derived from varia ..."
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Cited by 56 (1 self)
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We present a method of recovering shape from shading that solves directly for the surface height. By using a discrete formulation of the problem, we are able to achieve good convergence behavior by employing numerical solution techniques more powerful than gradient descent methods derived from variational calculus. Because we directly solve for height, we avoid the problem of finding an integrable surface maximally consistent with surface orientation. Furthermore, since we do not need additional constraints to make the problem well posed, we use a smoothness constraint only to drive the system towards a good solution; the weight of the smoothness term is eventually reduced to near zero. Also, by solving directly for height, we can use stereo processing to provide initial and boundary conditions. Our shape from shading technique, as well as its relation to stereo, is demonstrated on both synthetic and real imagery. 1 Introduction The problem of extracting shape from the shaded image of...
Image Morphing Using Deformation Techniques
, 1996
"... This paper presents a new image morphing method using a twodimensional deformation technique which provides an intuitive model for a warp. The deformation technique derives a C ..."
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Cited by 54 (6 self)
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This paper presents a new image morphing method using a twodimensional deformation technique which provides an intuitive model for a warp. The deformation technique derives a C