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75
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 356 (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.
A Minimum Description Length Approach to Statistical Shape Modelling
 IEEE Transactions on Medical Imaging
, 2001
"... We describe a method for automatically building statistical shape models from a training set of exam ple boundaries / surfaces. These models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of ..."
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Cited by 202 (12 self)
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We describe a method for automatically building statistical shape models from a training set of exam ple boundaries / surfaces. These models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of dense correspondences between all members of a set of training shapes. Often this is achieved by locating a set of qandmarks manually on each training image, which is timeconsuming and subjective in 2D, and almost impossible in 3D. We describe how shape models can be built automatically by posing the correspondence problem as one of finding the parameterization for each shape in the training set. We select the set of parameterizations that build the best model. We define best as that which min imizes the description length of the training set, arguing that this leads to models with good compactness, specificity and generalization ability. We show how a set of shape parameterizations can be represented and manipulated in order to build a minimum description length model. Results are given for several different training sets of 2D boundaries, showing that the proposed method constructs better models than other approaches including manual landmarking  the current gold standard. We also show that the method can be extended straightforwardly to 3D.
ShapeBased 3D Surface Correspondence Using Geodesics and Local Geometry
, 2000
"... This paper describes a new method for determining correspondence between points on pairs of surfaces based on shape using a combination of geodesic distance and surface curvature. An initial sparse set of corresponding points are generated using a shapebased matching procedure. Geodesic interpolatio ..."
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Cited by 69 (4 self)
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This paper describes a new method for determining correspondence between points on pairs of surfaces based on shape using a combination of geodesic distance and surface curvature. An initial sparse set of corresponding points are generated using a shapebased matching procedure. Geodesic interpolation is employed in order to capture the complex surface. In addition, surface correspondence and triangulation are computed simultaneously in a hierarchical way. Results applied to human cerebral cortical surfaces are shown to evaluate the approach. 1 Introduction Determining the correspondence of 3D points between pairs of surfaces has many important applications such as for comparing shape between deformable objects, nonrigid registration, developing probabilistic models and atlases, etc. While shape provides the basis for such a correspondence, this problem remains a di#cult one due to ambiguity when the surfaces are complex and variable, such as with the human cerebral cortex. In additi...
A CorrelationBased Approach to Robust Point Set Registration
 IN ECCV
, 2004
"... Correlation is a very effective way to align intensity images. We extend the correlation technique to point set registration using a method we call kernel correlation. Kernel correlation is an affinity measure, and it is also a function of the point set entropy. We define the point set registratio ..."
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Cited by 63 (1 self)
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Correlation is a very effective way to align intensity images. We extend the correlation technique to point set registration using a method we call kernel correlation. Kernel correlation is an affinity measure, and it is also a function of the point set entropy. We define the point set registration problem as finding the maximum kernel correlation configuration of the the two point sets to be registered. The new registration method has intuitive interpretations, simple to implement algorithm and easy to prove convergence property. Our method shows favorable performance when compared with the iterative closest point (ICP) and EMICP methods.
Efficient meanshift tracking via a new similarity measure
 in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’05
, 2005
"... The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are th ..."
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Cited by 52 (4 self)
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The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the KullbackLeibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the samplebased classical similarity measures require a calculation that is quadratic in the number of samples, making realtime performance difficult. To deal with these difficulties we propose a new, simpletocompute and more discriminative similarity measure in spatialfeature spaces. The new similarity measure allows the mean shift algorithm to track more general motion models in an integrated way. To reduce the complexity of the computation to linear order we employ the recently proposed improved fast Gauss transform. This leads to a very efficient and robust nonparametric spatialfeature tracking algorithm. The algorithm is tested on several image sequences and shown to achieve robust and reliable framerate tracking.
Hand Motion from 3D Point Trajectories and a Smooth Surface Model
 8TH EUROPEAN CONFERENCE ON COMPUTER VISION. VOLUME I OF LNCS 3021
, 2004
"... A method is proposed to track the full hand motion from 3D points on the surface of the hand that were reconstructed and tracked using a stereoscopic set of cameras. This approach combines the advantages of previous methods that use 2D motion (e.g. optical flow), and those that use a 3D reconstr ..."
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Cited by 35 (8 self)
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A method is proposed to track the full hand motion from 3D points on the surface of the hand that were reconstructed and tracked using a stereoscopic set of cameras. This approach combines the advantages of previous methods that use 2D motion (e.g. optical flow), and those that use a 3D reconstruction at each time frame to capture the hand motion. Matching either contours or a 3D reconstruction against a 3D hand model is usually very difficult due to selfocclusions and the locallycylindrical structure of each phalanx in the model, but our use of 3D point trajectories constrains the motion and overcomes these problems. Our tracking
Rigid Point Feature Registration Using Mutual Information
, 1999
"... We have developed a new mutual informationbased registration method for matching unlabeled point features. In contrast to earlier mutual informationbased registration methods which estimate the mutual information using image intensity information, our approach uses the point feature location infor ..."
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Cited by 35 (2 self)
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We have developed a new mutual informationbased registration method for matching unlabeled point features. In contrast to earlier mutual informationbased registration methods which estimate the mutual information using image intensity information, our approach uses the point feature location information. A novel aspect of our approach is the emergence of correspondence (between the two sets of features) as a natural byproduct of joint density estimation. We have applied this algorithm to the problem of geometric alignment of primate autoradiographs. We also present preliminary results on 3D robust matching of sulci derived from anatomical MR. Finally, we present an experimental comparison between the mutual information approach and other recent approaches which explicitly parameterize feature correspondence. Keywords: point feature registration, rigid alignment, mutual information, similarity transformation, spatial mapping, correspondence, joint probability, softassign Received ?...
Automatic Construction of 2D Shape Models
, 2001
"... A procedure for automated 2D shape model design is presented. The modeling system is given a set of training example shapes dened by the coordinates of their contour points. The shapes are automatically aligned using Procrustes analysis, and clustered to obtain cluster prototypes (typical objects) a ..."
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Cited by 34 (3 self)
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A procedure for automated 2D shape model design is presented. The modeling system is given a set of training example shapes dened by the coordinates of their contour points. The shapes are automatically aligned using Procrustes analysis, and clustered to obtain cluster prototypes (typical objects) and statistical information about intracluster shape variation. One dierence from previously reported methods is that the training set is rst automatically clustered and those shapes considered to be outliers are discarded. In this way, the cluster prototypes are not distorted by outlier shapes. A second dierence is in the manner in which registered sets of points are extracted from each shape contour. We propose a exible point matching technique that takes into account both pose/scale dierences as well as nonlinear shape dierences between a pair of objects. The matching method is independent of the initial relative position/scale of the two objects and does not require any manually ...
Probabilistic CurveAligned Clustering and Prediction with Regression Mixture Models
 Ph.D. Dissertation, 2004. Laboratoire MAS
, 2004
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