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68
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 ..."
Abstract

Cited by 964 (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....
A New Point Matching Algorithm for NonRigid Registration
, 2002
"... Featurebased methods for nonrigid registration frequently encounter the correspondence problem. Regardless of whether points, lines, curves or surface parameterizations are used, featurebased nonrigid matching requires us to automatically solve for correspondences between two sets of features. I ..."
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Cited by 362 (3 self)
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Featurebased methods for nonrigid registration frequently encounter the correspondence problem. Regardless of whether points, lines, curves or surface parameterizations are used, featurebased nonrigid matching requires us to automatically solve for correspondences between two sets of features. In addition, there could be many features in either set that have no counterparts in the other. This outlier rejection problem further complicates an already di#cult correspondence problem. We formulate featurebased nonrigid registration as a nonrigid point matching problem. After a careful review of the problem and an indepth examination of two types of methods previously designed for rigid robust point matching (RPM), we propose a new general framework for nonrigid point matching. We consider it a general framework because it does not depend on any particular form of spatial mapping. We have also developed an algorithmthe TPSRPM algorithmwith the thinplate spline (TPS) as the parameterization of the nonrigid spatial mapping and the softassign for the correspondence. The performance of the TPSRPM algorithm is demonstrated and validated in a series of carefully designed synthetic experiments. In each of these experiments, an empirical comparison with the popular iterated closest point (ICP) algorithm is also provided. Finally, we apply the algorithm to the problem of nonrigid registration of cortical anatomical structures which is required in brain mapping. While these results are somewhat preliminary, they clearly demonstrate the applicability of our approach to real world tasks involving featurebased nonrigid registration.
Error Modeling in Stereo Navigation
, 1987
"... In stereo navigation, a mobile robot estimates its position by tracking landmarks with onboard cameras. Previous systems for stereo navigation have suffered from poor accuracy, in parl because they relied on scalar models of measurement error in triangulation. Using threedimensional (3D) Gaussian d ..."
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Cited by 166 (13 self)
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In stereo navigation, a mobile robot estimates its position by tracking landmarks with onboard cameras. Previous systems for stereo navigation have suffered from poor accuracy, in parl because they relied on scalar models of measurement error in triangulation. Using threedimensional (3D) Gaussian distributions to model triangulation error is shown to lead Io much better performance. How to compute fhe error model from image correspondences, estimate robot motion between frames, and update the global positions of the robot and the landmarks over time are discussed. Simulations show that, compared to scalar error models, the 3D Gaussian reduces the variance in robot position estimates and better distinguishes rotational from translational motion. A shorl indoor run with real images supporled these conclusions and computed' the final robot position to within two percent of distance and one degree of orientation. These results illustrate the imporlanee of error modeling in stereo vision for this and other applications.
New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence
"... A fundamental open problem in computer visiondetermining pose and correspondence between two sets of points in spaceis solved with a novel, fast [O(nm)], robust and easily implementable algorithm. The technique works on noisy 2D or 3D point sets that may be of unequal sizes and may differ by n ..."
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Cited by 104 (20 self)
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A fundamental open problem in computer visiondetermining pose and correspondence between two sets of points in spaceis solved with a novel, fast [O(nm)], robust and easily implementable algorithm. The technique works on noisy 2D or 3D point sets that may be of unequal sizes and may differ by nonrigid transformations. Using a combination of optimization techniques such as deterministic annealing and the softassign, which have recently emerged out of the recurrent neural network/statistical physics framework, analog objective functions describing the problems are minimized. Over thirty thousand experiments, on randomly generated points sets with varying amounts of noise and missing and spurious points, and on handwritten character sets demonstrate the robustness of the algorithm. Keywords: Pointmatching, pose estimation, correspondence, neural networks, optimization, softassign, deterministic annealing, affine. 1 Introduction Matching the representations of two images has long...
A Robust Point Matching Algorithm for Autoradiograph Alignment
, 1997
"... We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the onetoone correspondences (or homologies) between point features extracted from the images and rejecting nonhomologies as outliers. In this way, we atte ..."
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Cited by 49 (12 self)
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We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the onetoone correspondences (or homologies) between point features extracted from the images and rejecting nonhomologies as outliers. In this way, we attempt to account for the local natural and artifactual differences between the autoradiograph slices. We have executed the resulting automated algorithm on a set of left prefrontal cortex autoradiograph slices, specifically demonstrated its ability to perform point outlier rejection, validated it using synthetically generated spatial mappings and provided a visual comparison against the well known iterated closest point (ICP) algorithm. Visualization of a stack of aligned left prefrontal cortex autoradiograph slices is also provided.
Optimal Geometric Model Matching Under Full 3D Perspective
, 1994
"... Modelbased object recognition systems have rarely dealt directly with 3D perspective while matching models to images. The algorithms presented here use 3D pose recovery during matching to explicitly and quantitatively account for changes in model appearance associated with 3D perspective. These alg ..."
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Cited by 39 (16 self)
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Modelbased object recognition systems have rarely dealt directly with 3D perspective while matching models to images. The algorithms presented here use 3D pose recovery during matching to explicitly and quantitatively account for changes in model appearance associated with 3D perspective. These algorithms use randomstart local search to find, with high probability, the globally optimal correspondence between model and image features in spaces containing over 2 100 possible matches. Three specific algorithms are compared on robot landmark recognition problems. A fullperspective algorithm uses the 3D pose algorithm in all stages of search while two hybrid algorithms use a computationally less demanding weakperspective procedure to rank alternative matches and updates 3D pose only when moving to a new match. These hybrids successfully solve problems involving perspective, and in less time than required by the fullperspective algorithm.
Recognition Using Region Correspondences
 International Journal of Computer Vision
, 1995
"... A central problem in object recognition is to determine the transformation that relates the model to the image, given some partial correspondence between the two. This is useful in determining whether an object is present in an image, and if so, determining where the object is. We present a novel me ..."
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Cited by 38 (7 self)
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A central problem in object recognition is to determine the transformation that relates the model to the image, given some partial correspondence between the two. This is useful in determining whether an object is present in an image, and if so, determining where the object is. We present a novel method of solving this problem that uses region information. In our approach the model is divided into volumes, and the image is divided into regions. Given a match between subsets of volumes and regions (without any explicit correspondence between different pieces of the regions) the alignment transformation is computed. The method applies to planar objects under similarity, affine, and projective transformations and to projections of 3D objects undergoing affine and projective transformations. 1 Introduction A fundamental problem in recognition is pose estimation. Given a correspondence between some portions of an object model and some portions of an image, determine the transformation th...
How easy is matching 2D line models using local search?
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... Local search is a well established and highly effective method for solving complex combinatorial optimization problems. Here, local search is adapted to solve difficult geometric matching problems. Matching is posed as the problem of finding the optimal manytomany correspondence mapping between a ..."
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Cited by 35 (3 self)
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Local search is a well established and highly effective method for solving complex combinatorial optimization problems. Here, local search is adapted to solve difficult geometric matching problems. Matching is posed as the problem of finding the optimal manytomany correspondence mapping between a line segment model and image line segments. Image data is assumed to be fragmented, noisy, and cluttered. The algorithms presented have been used for robot navigation, photo interpretation, and scene understanding. This paper explores how local search performs as model complexity increases, image clutter increases, and additional model instances are added to the image data. Expected runtimes to find optimal matches with 95 percent confidence are determined for 48 distinct problems involving six models. Nonlinear regression is used to estimate runtime growth as a function of problem size. Both polynomial and exponential growth models are fit to the runtime data. For problems with random clutter, the polynomial model fits better and growth is comparable to that for tree search. For problems involving symmetric models and multiple model instances, where tree search is exponential, the polynomial growth model is superior to the exponential growth model for one search algorithm and comparable for another.
A Probabilistic Formulation for Hausdorff Matching
 In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
, 1998
"... Matching images based on a Hausdorff measure has become popular for computer vision applications. However, no probabilistic model has been used in these applications. This limits the formal treatment of several issues, such as feature uncertainties and prior knowledge. In this paper, we develop a pr ..."
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Cited by 24 (3 self)
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Matching images based on a Hausdorff measure has become popular for computer vision applications. However, no probabilistic model has been used in these applications. This limits the formal treatment of several issues, such as feature uncertainties and prior knowledge. In this paper, we develop a probabilistic formulation of image matching in terms of maximum likelihood estimation that generalizes a version of Hausdorff matching. This formulation yields several benefits with respect to previous Hausdorff matching formulations. In addition, we show that the optimal model position in a discretized pose space can be located efficiently in this formation and we apply these techniques to a mobile robot selflocalization problem. 1 Introduction The use of variants of the Hausdorff distance has recently become popular for image matching applications (see, for example, [6, 9, 11, 16, 18, 19]). While these methods have been largely successful, they have lacked a probabilistic formulation of th...
An Approach to Object Recognition: Aligning Pictorial Descriptions
 Laboratory, Massachusetts Institute of Technology
, 1986
"... This paper examines the problem of shapebased object recognition, and proposes a new approach, the alignment of pictorial descriptions. ..."
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Cited by 22 (4 self)
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This paper examines the problem of shapebased object recognition, and proposes a new approach, the alignment of pictorial descriptions.