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21
Iterative point matching for registration of freeform curves and surfaces
, 1994
"... A heuristic method has been developed for registering two sets of 3D curves obtained by using an edgebased stereo system, or two dense 3D maps obtained by using a correlationbased stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in ma ..."
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Cited by 486 (6 self)
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A heuristic method has been developed for registering two sets of 3D curves obtained by using an edgebased stereo system, or two dense 3D maps obtained by using a correlationbased stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in many practical applications, some a priori knowledge exists which considerably simplifies the problem. In visual navigation, for example, the motion between successive positions is usually approximately known. From this initial estimate, our algorithm computes observer motion with very good precision, which is required for environment modeling (e.g., building a Digital Elevation Map). Objects are represented by a set of 3D points, which are considered as the samples of a surface. No constraint is imposed on the form of the objects. The proposed algorithm is based on iteratively matching points in one set to the closest points in the other. A statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which allows us to do subsetsubset matching. A leastsquares technique is used to estimate 3D motion from the point correspondences, which reduces the average distance between points in the two sets. Both synthetic and real data have been used to test the algorithm, and the results show that it is efficient and robust, and yields an accurate motion estimate.
The dualbootstrap iterative closest point algorithm with application to retinal image registration
 IEEE Trans. Med. Img
, 2003
"... Abstract—Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called DualBootstrap Iterative Closest Point (DualBootstrap ICP). The approach is to start from one or more initial, loworder estimates that are only accurate in small ..."
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Cited by 57 (18 self)
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Abstract—Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called DualBootstrap Iterative Closest Point (DualBootstrap ICP). The approach is to start from one or more initial, loworder estimates that are only accurate in small image regions, called bootstrap regions. In each bootstrap region, the algorithm iteratively: 1) refines the transformation estimate using constraints only from within the bootstrap region; 2) expands the bootstrap region; and 3) tests to see if a higher order transformation model can be used, stopping when the region expands to cover the overlap between images. Steps 1): and 3), the bootstrap steps, are governed by the covariance matrix of the estimated transformation. Estimation refinement [Step 2)] uses a novel robust version of the ICP algorithm. In registering retinal image pairs, DualBootstrap ICP is initialized by automatically matching individual vascular landmarks, and it aligns images based on detected blood vessel centerlines. The resulting quadratic transformations are accurate to less than a pixel. On tests involving approximately 6000 image pairs, it successfully registered 99.5 % of the pairs containing at least one common landmark, and 100 % of the pairs containing at least one common landmark and at least 35 % image overlap. Index Terms—Iterative closest point, medical imaging, registration, retinal imaging, robust estimation.
Geometrically Stable Sampling for the ICP Algorithm
 Proc. International Conference on 3D Digital Imaging and Modeling
, 2003
"... The Iterative Closest Point (ICP) algorithm is a widely used method for aligning threedimensional point sets. The quality of alignment obtained by this algorithm depends heavily on choosing good pairs of corresponding points in the two datasets. If too many points are chosen from featureless region ..."
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Cited by 48 (5 self)
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The Iterative Closest Point (ICP) algorithm is a widely used method for aligning threedimensional point sets. The quality of alignment obtained by this algorithm depends heavily on choosing good pairs of corresponding points in the two datasets. If too many points are chosen from featureless regions of the data, the algorithm converges slowly, finds the wrong pose, or even diverges, especially in the presence of noise or miscalibration in the input data. In this paper, we describe a method for detecting uncertainty in pose, and we propose a point selection strategy for ICP that minimizes this uncertainty by choosing samples that constrain potentially unstable transformations.
3D2D projective registration of freeform curves and surfaces
, 1994
"... : Some medical interventions require knowing the correspondence between an MRI/CT image and the actual position of the patient. Examples occur in neurosurgery and radiotherapy, but also in video surgery (laparoscopy). We present in this paper three new techniques for performing this task without art ..."
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Cited by 42 (4 self)
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: Some medical interventions require knowing the correspondence between an MRI/CT image and the actual position of the patient. Examples occur in neurosurgery and radiotherapy, but also in video surgery (laparoscopy). We present in this paper three new techniques for performing this task without artificial markers. To do this, we find the 3D2D projective transformation (composition of a rigid displacement and a perspective projection) which maps a 3D object onto a 2D image of this object. Depending on the object model (curve or surface), and on the 2D image acquisition system (XRay, video), the techniques are different but the framework is common: ffl We first find an estimate of the transformation using bitangent lines or bitangent planes. These are first order semidifferential invariants [GMPO92]. ffl Then, introducing the normal or tangent, we define a distance between the 3D object and the 2D image, and we minimize it using extensions of the Iterative Closest Point algorithm (...
Techniques for fast and accurate intrasurgical registration
 Journal of image guided surgery
, 1995
"... The goal of intrasurgical registration is to establish a common reference frame between presurgical and intrasurgical 3D data sets that correspond to the same anatomy. This paper presents two novel techniques which have application to this problem: highspeed pose tracking, and intrasurgical da ..."
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Cited by 30 (11 self)
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The goal of intrasurgical registration is to establish a common reference frame between presurgical and intrasurgical 3D data sets that correspond to the same anatomy. This paper presents two novel techniques which have application to this problem: highspeed pose tracking, and intrasurgical data selection. In the first part of this paper, we describe an approach for tracking the pose of arbitrarilyshaped rigid objects at rates up to 10Hz. Static accuracies on the order of 1mm in translation and 1 degree in rotation have been achieved. We have demonstrated the technique on a human face using a highspeed VLSI range sensor; however, the technique is independent of the sensor used or the anatomy tracked. In the second part of this paper, we describe a general purpose approach for selecting nearoptimal, intrasurgical registration data. Due to high costs associated with the acquisition of intrasurgical data, it is desirable to minimize the amount of data acquired, while ensuring that registration accuracy requirements are met. We synthesize nearoptimal intrasurgical data sets, based upon an analysis of differential surface properties of presurgical data. We demonstrate, using data from a human femur, that discrete point data sets selected using our method provide superior pose refinement accuracy to those selected by
Extension of the ICP algorithm to nonrigid intensitybased registration of 3D volumes
 COMPUT. VIS. IMAGE UNDERSTANDING
, 1997
"... We present in this paper a new registration and gain correction algorithm for 3D medical images. It is intensity based. The basic idea is to represent images by 4D points (xj;yj;zj;ij) and to define a global energy function based on this representation. For minimisation, we propose a technique which ..."
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Cited by 30 (5 self)
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We present in this paper a new registration and gain correction algorithm for 3D medical images. It is intensity based. The basic idea is to represent images by 4D points (xj;yj;zj;ij) and to define a global energy function based on this representation. For minimisation, we propose a technique which does not require computing the derivatives of this criterion with respect to the parameters. It can be understood as an extension of the Iterative Closest Point algorithm [5, 56] or as an application of the formalism proposed in [13]. Two parameters enable us to develop a coarsetofine strategy both for resolution and for deformation. Our technique presents the advantage of minimising a welldefined global criterion, to deal with various classes of transformations (for example rigid, affine, volume spline and radial basis functions), to be simple to implement, and to be efficient in practice. Results on real brain and heart 3D images are presented to demonstrate the validity of our approach. We also explain how one can compute basic statistics on the deformation parameters to constrain the set of possible deformations by learning and to discriminate between different groups.
Freeform Surface Matching for Surface Inspection
 in The Mathematics of Surfaces VI
, 1994
"... Despite their great accuracy, coordinate measuring machines (CMM)... ..."
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Cited by 6 (0 self)
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Despite their great accuracy, coordinate measuring machines (CMM)...
Fast and Robust Registration of 3D Surfaces Using Low Curvature Patches
 IN PROC. 2ND INT. CONF. ON 3D DIGITAL IMAGING AND MODELING
, 1999
"... This paper describes a new model to range data registration algorithm, specifically designed for accuracy, speed, and robustness. Like many recent registration techniques, our RobustClosestPatch algorithm (RCP) iteratively matches model patches to data surfaces based on the current pose and then r ..."
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Cited by 5 (2 self)
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This paper describes a new model to range data registration algorithm, specifically designed for accuracy, speed, and robustness. Like many recent registration techniques, our RobustClosestPatch algorithm (RCP) iteratively matches model patches to data surfaces based on the current pose and then reestimates pose based on these matches. RCP has several novel features: 1) online registration is driven by low curvature patches computed from the model offline; 2) an approximate normal distance between a patch and a surface is used, avoiding the need to estimate local surface normal and curvature from noisy data; 3) pose is solved exactly by a linear system in six parameters, using a symmetric formulation of the rotation constraint; 4) robustness is ensured using an Mestimator that estimates both the rigid pose parameters and the error standard deviation. Results are shown using models and range data from turbine blade inspection.
Registration of combined range–intensity scans: Initialization
, 2007
"... Available online at www.sciencedirect.com ..."
Geometric Constraint Analysis and Synthesis: Methods for Improving ShapeBased Registration Accuracy
 In Proc. 1st Joint CVRMed / MRCAS
, 1997
"... . Shapebased registration is a process for estimating the transformation between two shape representations of an object. It is used in many imageguided surgical systems to establish a transformation between pre and intraoperative coordinate systems. This paper describes several tools which are u ..."
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Cited by 3 (2 self)
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. Shapebased registration is a process for estimating the transformation between two shape representations of an object. It is used in many imageguided surgical systems to establish a transformation between pre and intraoperative coordinate systems. This paper describes several tools which are useful for improving the accuracy resulting from shapebased registration: constraint analysis, constraint synthesis, and online accuracy estimation. Constraint analysis provides a scalar measure of sensitivity which is well correlated with registration accuracy. This measure can be used as a criterion function by constraint synthesis, an optimization process which generates configurations of registration data which maximize expected accuracy. Online accuracy estimation uses a conventional rootmean squared error measure coupled with constraint analysis to estimate an upper bound on true registration error. This paper demonstrates that registration accuracy can be significantly improved via a...