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61
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 239 (2 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.
A New Algorithm for NonRigid Point Matching
 IN CVPR
, 2000
"... We present a new robust point matching algorithm (RPM) that can jointly estimate the correspondence and nonrigid transformations between two pointsets that may be of different sizes. The algorithm utilizes the softassign for the correspondence and the thinplate spline for the nonrigid mapping. E ..."
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Cited by 160 (7 self)
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We present a new robust point matching algorithm (RPM) that can jointly estimate the correspondence and nonrigid transformations between two pointsets that may be of different sizes. The algorithm utilizes the softassign for the correspondence and the thinplate spline for the nonrigid mapping. Embedded within a deterministic annealing framework, the algorithm can automatically reject a fraction of the points as outliers. Experiments on both 2D synthetic pointsets with varying degrees of deformation, noise and outliers, and on real 3D sulcal pointsets (extracted from brain MRI) demonstrate the robustness of the algorithm.
Image Change Detection Algorithms: A Systematic Survey
 IEEE Transactions on Image Processing
, 2005
"... Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. T ..."
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Cited by 110 (2 self)
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Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.
Robust Registration of 2D and 3D Point Sets
, 2001
"... This paper introduces a new method of registering point sets. The registration error is directly minimized using generalpurpose nonlinear optimization (the LevenbergMarquardt algorithm). The surprising conclusion of the paper is that this technique is comparable in speed to the specialpurpose ICP ..."
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Cited by 94 (0 self)
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This paper introduces a new method of registering point sets. The registration error is directly minimized using generalpurpose nonlinear optimization (the LevenbergMarquardt algorithm). The surprising conclusion of the paper is that this technique is comparable in speed to the specialpurpose ICP algorithm which is most commonly used for this task. Because the routine directly minimizes an energy function, it is easy to extend it to incorporate robust estimation via a Huber kernel, yielding a basin of convergence that is many times wider than existing techniques. Finally we introduce a data structure for the minimization based on the chamfer distance transform which yields an algorithm which is both faster and more robust than previously described methods.
A Statistical Approach to 3D Object Detection Applied to Faces and Cars
, 2000
"... In this thesis, we describe a statistical method for 3D object detection. In this method, we decompose the 3D geometry of each object into a small number of viewpoints. For each viewpoint, we construct a decision rule that determines if the object is present at that specific orientation. Each decisi ..."
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Cited by 85 (1 self)
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In this thesis, we describe a statistical method for 3D object detection. In this method, we decompose the 3D geometry of each object into a small number of viewpoints. For each viewpoint, we construct a decision rule that determines if the object is present at that specific orientation. Each decision rule uses the statistics of both object appearance and &quot;nonobject &quot; visual appearance. We represent each set of statistics using a product of histograms. Each histogram represents the joint statistics of a subset of wavelet coefficients and their position on the object. Our approach is to use many such histograms representing a wide variety of visual attributes. Using this method, we have developed the first algorithm that can reliably detect faces that vary from frontal view to full profile view and the first algorithm that can reliably detect cars over a wide range of viewpoints.
Multiscale EMICP: A Fast and Robust Approach for Surface Registration
 European Conference on Computer Vision (ECCV 2002), volume 2353 of LNCS
, 2002
"... We investigate in this article the rigid registration of large sets of points, generally sampled from surfaces. We formulate this problem as a general MaximumLikelihood (ML) estimation of the transformation and the matches. We show that, in the specific case of a Gaussian noise, it corresponds to t ..."
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Cited by 62 (6 self)
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We investigate in this article the rigid registration of large sets of points, generally sampled from surfaces. We formulate this problem as a general MaximumLikelihood (ML) estimation of the transformation and the matches. We show that, in the specific case of a Gaussian noise, it corresponds to the Iterative Closest Point algorithm (ICP) with the Mahalanobis distance.
Nonrigid point set registration: Coherent Point Drift (CPD)
 IN ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 19
, 2006
"... We introduce Coherent Point Drift (CPD), a novel probabilistic method for nonrigid registration of point sets. The registration is treated as a Maximum Likelihood (ML) estimation problem with motion coherence constraint over the velocity field such that one point set moves coherently to align with ..."
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Cited by 55 (0 self)
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We introduce Coherent Point Drift (CPD), a novel probabilistic method for nonrigid registration of point sets. The registration is treated as a Maximum Likelihood (ML) estimation problem with motion coherence constraint over the velocity field such that one point set moves coherently to align with the second set. We formulate the motion coherence constraint and derive a solution of regularized ML estimation through the variational approach, which leads to an elegant kernel form. We also derive the EM algorithm for the penalized ML optimization with deterministic annealing. The CPD method simultaneously finds both the nonrigid transformation and the correspondence between two point sets without making any prior assumption of the transformation model except that of motion coherence. This method can estimate complex nonlinear nonrigid transformations, and is shown to be accurate on 2D and 3D examples and robust in the presence of outliers and missing points.
Articulated Shape Matching Using Laplacian Eigenfunctions and Unsupervised Point Registration
"... Matching articulated shapes represented by voxelsets reduces to maximal subgraph isomorphism when each set is described by a weighted graph. Spectral graph theory can be used to map these graphs onto lower dimensional spaces and match shapes by aligning their embeddings in virtue of their invarian ..."
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Cited by 47 (11 self)
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Matching articulated shapes represented by voxelsets reduces to maximal subgraph isomorphism when each set is described by a weighted graph. Spectral graph theory can be used to map these graphs onto lower dimensional spaces and match shapes by aligning their embeddings in virtue of their invariance to change of pose. Classical graph isomorphism schemes relying on the ordering of the eigenvalues to align the eigenspaces fail when handling large datasets or noisy data. We derive a new formulation that finds the best alignment between two congruent Kdimensional sets of points by selecting the best subset of eigenfunctions of the Laplacian matrix. The selection is done by matching eigenfunction signatures built with histograms, and the retained set provides a smart initialization for the alignment problem with a considerable impact on the overall performance. Dense shape matching casted into graph matching reduces then, to point registration of embeddings under orthogonal transformations; the registration is solved using the framework of unsupervised clustering and the EM algorithm. Maximal subset matching of non identical shapes is handled by defining an appropriate outlier class. Experimental results on challenging examples show how the algorithm naturally treats changes of topology, shape variations and different sampling densities. 1.
Two Geometric Algorithms for Layout Analysis
 In Workshop on Document Analysis Systems
, 2002
"... This paper presents geometric algorithms for solving two key problems in layout analysis: finding a cover of the background whitespace of a document in terms of maximal empty rectangles, and finding constrained maximum likelihood matches of geometric text line models in the presence of geometric ..."
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Cited by 45 (11 self)
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This paper presents geometric algorithms for solving two key problems in layout analysis: finding a cover of the background whitespace of a document in terms of maximal empty rectangles, and finding constrained maximum likelihood matches of geometric text line models in the presence of geometric obstacles.
A Feature Registration Framework using Mixture Models
 In Proc. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
"... We formulate feature registration problems as maximum likelihood or Bayesian maximum a posteriori estimation problems using mixture models. An EMlike algorithm is proposed to jointly solve for the feature correspondences as well as the geometric transformations. A novel aspect of our approach is th ..."
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Cited by 26 (1 self)
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We formulate feature registration problems as maximum likelihood or Bayesian maximum a posteriori estimation problems using mixture models. An EMlike algorithm is proposed to jointly solve for the feature correspondences as well as the geometric transformations. A novel aspect of our approach is the embedding of the EM algorithm within a deterministic annealing scheme in order to directly control the fuzziness of the correspondences. The resulting algorithm  termed mixture point matching (MPM)  can solve for both rigid and high dimensional (thinplate splinebased) nonrigid transformations between point sets in the presence of noise and outliers. We demonstrate the algorithm's performance on 2D and 3D data.