Results 1  10
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19
A Survey of Image Registration Techniques
 ACM Computing Surveys
, 1992
"... 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 ..."
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Cited by 698 (2 self)
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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....
Scattered Data Interpolation with Multilevel Splines
 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
, 1997
"... This paper describes a fast algorithm for scattered data interpolation and approximation. Multilevel Bsplines are introduced to compute a C²continuous surface through a set of irregularly spaced points. The algorithm makes use of a coarsetofine hierarchy of control lattices to generate a sequen ..."
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Cited by 106 (9 self)
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This paper describes a fast algorithm for scattered data interpolation and approximation. Multilevel Bsplines are introduced to compute a C²continuous surface through a set of irregularly spaced points. The algorithm makes use of a coarsetofine hierarchy of control lattices to generate a sequence of bicubic Bspline functions whose sum approaches the desired interpolation function. Large performance gains are realized by using Bspline refinement to reduce the sum of these functions into one equivalent Bspline function. Experimental results demonstrate that highfidelity reconstruction is possible from a selected set of sparse and irregular samples.
Automatic Smoothing Spline Projection Pursuit
 Journal of Computational and Graphical Statistics
, 1994
"... A highly flexible nonparametric regression model for predicting a response y given covariates fx k g d k=1 is the projection pursuit regression (PPR) model y = h(x) = fi 0 + P j fi j f j (ff T j x), where the f j are general smooth functions with mean zero and norm one, and P d k=1 ff 2 k ..."
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Cited by 17 (1 self)
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A highly flexible nonparametric regression model for predicting a response y given covariates fx k g d k=1 is the projection pursuit regression (PPR) model y = h(x) = fi 0 + P j fi j f j (ff T j x), where the f j are general smooth functions with mean zero and norm one, and P d k=1 ff 2 kj = 1. The standard PPR algorithm of Friedman and Stuetzle (1981) estimates the smooth functions f j using the supersmoother nonparametric scatterplot smoother. Friedman's algorithm constructs a model with M max linear combinations, then prunes back to a simpler model of size M M max , where M and M max are specified by the user. This paper discusses an alternative algorithm in which the smooth functions are estimated using smoothing splines. The direction coefficients ff j , the amount of smoothing in each direction, and the number of terms M and M max are determined to optimize a single generalized crossvalidation measure. To appear as: Roosen, C. B. and Hastie, T. J. (1994). Automat...
Local hybrid approximation for scattered data fitting with bivariate splines
 Comput. Aided Geom. Design
, 2006
"... We suggest a local hybrid approximation scheme based on polynomials and radial basis functions, and use it to improve the scattered data fitting algorithm of [7]. Similar to that algorithm, the new method has linear computational complexity and is therefore suitable for large real world data. Numeri ..."
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Cited by 3 (2 self)
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We suggest a local hybrid approximation scheme based on polynomials and radial basis functions, and use it to improve the scattered data fitting algorithm of [7]. Similar to that algorithm, the new method has linear computational complexity and is therefore suitable for large real world data. Numerical examples suggest that it can produce high quality artifact–free approximations that are more accurate than those given by the original method where pure polynomial local approximations are used.
Image Registration using Multiquadric Functions, the Finite Element Method, Bivariate Mapping Polynomials and the Thin Plate Spline
, 1996
"... In this report, three methods of imagetoimage registration using control points are evaluated. We assume that ephemeris sensor and platform data are unavailable. These techniques are the polynomial method, the piecewise linear transformation and the multiquadric method. The motivation for this res ..."
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Cited by 2 (0 self)
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In this report, three methods of imagetoimage registration using control points are evaluated. We assume that ephemeris sensor and platform data are unavailable. These techniques are the polynomial method, the piecewise linear transformation and the multiquadric method. The motivation for this research is the need for more accurate geometric correction of digital remote sensing data. This is especially important for airborne scanned imagery which is characterized by greater distortions than satellite data. The polynomial and piecewise linear methods were developed for use with satellite imagery and have remained popular due to their relative simplicity in theory and implementation. With respect to airborne data however, both of these methods have serious shortcomings. The polynomial method, a global model, is generally applied as a leastsquares approximation to the control points. Mathematically it is unconstrained between points leading to undesirable excursions in the warp. The p...
Getting better contour plots with S and GCVPACK
, 1990
"... Abstract: We show how to obtain esthetically pleasing contour plots using New S and GCVPACK. With these codes, thin plate splines can easily be used to interpolate “exact ” data, and to produce smoothly varying contour plots, with none of the jagged corners that plague many other interpolation metho ..."
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Cited by 2 (0 self)
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Abstract: We show how to obtain esthetically pleasing contour plots using New S and GCVPACK. With these codes, thin plate splines can easily be used to interpolate “exact ” data, and to produce smoothly varying contour plots, with none of the jagged corners that plague many other interpolation methods. It is noted that GCVPACK can also be used to interpolate data on the sphere and in Euclidean three space. We observe that a larger class of global interpolation methods (including the thin plate spline) have a Bayesian interpretation, and GCVPACK can be used to compute them.
Towards Automatic Registration of Magnetic Resonance Images of the Brain Using Neural Networks. Part 2
, 1998
"... 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 ..."
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Cited by 1 (1 self)
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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 thresholdlike, feature postprocessing 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
AN EXTENSION OF CHEBFUN TO TWO DIMENSIONS
"... Abstract. An objectoriented Matlab system is described that extends the capabilities of Chebfun to smooth functions of two variables defined on rectangles. Functions are approximated to essentially machine precision by using iterative Gaussian elimination with complete pivoting to form “chebfun2 ” ..."
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Abstract. An objectoriented Matlab system is described that extends the capabilities of Chebfun to smooth functions of two variables defined on rectangles. Functions are approximated to essentially machine precision by using iterative Gaussian elimination with complete pivoting to form “chebfun2 ” objects representing low rank approximations. Operations such as integration, differentiation, function evaluation, and transforms are particularly efficient. Global optimization, the singular value decomposition, and rootfinding are also extended to chebfun2 objects. Numerical applications are presented. Key words. Matlab, Chebfun, Chebyshev polynomials, low rank approximation