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27
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 480 (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.
An Automatic Registration Method for Frameless Stereotaxy, Image Guided Surgery, and Enhanced Reality Visualization
, 1996
"... There is a need for frameless guidance systems to help surgeons plan the exact location for incisions, to define the margins of tumors and to precisely identify locations of neighboring critical structures. We have developed an automatic technique for registering clinical data, such as segmented M ..."
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Cited by 106 (12 self)
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There is a need for frameless guidance systems to help surgeons plan the exact location for incisions, to define the margins of tumors and to precisely identify locations of neighboring critical structures. We have developed an automatic technique for registering clinical data, such as segmented MRI or CT reconstructions, with any view of the patient on the operating table, using a series of registration algorithms, which we demonstrate on the specific example of neurosurgery. The method enables a visual mix of live video of the patient with the segmented 3D MRI or CT model, supporting enhanced reality techniques for planning and guiding neurosurgical procedures, and to interactively view extracranial or intracranial structures nonintrusively. Extensions of the method include image guided biopsies, focused therapeutic procedures and clinical studies involving change detection over time sequences of images. 1 Artificial Intelligence Laboratory, Massachusetts Institute of Tech...
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 90 (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.
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.
Registration of Point Cloud Data from a Geometric Optimization Perspective
, 2004
"... We propose a framework for pairwise registration of shapes represented by point cloud data (PCD). We assume that the points are sampled from a surface and formulate the problem of aligning two PCDs as a minimization of the squared distance between the underlying surfaces. Local quadratic approximant ..."
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Cited by 49 (14 self)
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We propose a framework for pairwise registration of shapes represented by point cloud data (PCD). We assume that the points are sampled from a surface and formulate the problem of aligning two PCDs as a minimization of the squared distance between the underlying surfaces. Local quadratic approximants of the squared distance function are used to develop a linear system whose solution gives the best aligning rigid transform for the given pair of point clouds. The rigid transform is applied and the linear system corresponding to the new orientation is build. This process is iterated until it converges. The pointtopoint and the pointtoplane Iterated Closest Point (ICP) algorithms can be treated as special cases in this framework. Our algorithm can align PCDs even when they are placed far apart, and is experimentally found to be more stable than pointtoplane ICP. We analyze the convergence behavior of our algorithm and of pointtopoint and pointtoplane ICP under our proposed framework, and derive bounds on their rate of convergence. We compare the stability and convergence properties of our algorithm with other registration algorithms on a variety of scanned data.
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 (...
Surface Reconstruction and Display from Range and Color Data
, 1997
"... This dissertation addresses the problem of scanning both the color and geometry of real objects and displaying realistic images of the scanned objects from arbitrary viewpoints. We present a complete system that uses a stereo camera system with active lighting to scan the object surface geometry and ..."
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Cited by 33 (5 self)
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This dissertation addresses the problem of scanning both the color and geometry of real objects and displaying realistic images of the scanned objects from arbitrary viewpoints. We present a complete system that uses a stereo camera system with active lighting to scan the object surface geometry and color as visible from one point of view. Scans expressed in sensor coordinates are registered into a single objectcentered coordinate system by aligning both the color and geometry where the scans overlap. The range data are integrated into a surface model using a robust hierarchical space carving method. The fit of the resulting approximate mesh to data is improved and the mesh structure is simplified using mesh optimization methods. In addition, two methods are developed for viewdependent display of the reconstructed...
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.
Registration of Challenging Image Pairs: Initialization, Estimation, and Decision
, 2007
"... Our goal is an automated 2Dimagepair registration algorithm capable of aligning images taken of a wide variety of natural and manmade scenes as well as many medical images. The algorithm should handle low overlap, substantial orientation and scale differences, large illumination variations, and p ..."
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Cited by 22 (4 self)
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Our goal is an automated 2Dimagepair registration algorithm capable of aligning images taken of a wide variety of natural and manmade scenes as well as many medical images. The algorithm should handle low overlap, substantial orientation and scale differences, large illumination variations, and physical changes in the scene. An important component of this is the ability to automatically reject pairs that have no overlap or have too many differences to be aligned well. We propose a complete algorithm including techniques for initialization, for estimating transformation parameters, and for automatically deciding if an estimate is correct. Keypoints extracted and matched between images are used to generate initial similarity transform estimates, each accurate over a small region. These initial estimates are rankordered and tested individually in succession. Each estimate is refined using the DualBootstrap ICP algorithm, driven by matching of multiscale features. A threepart decision criteria, combining measurements of alignment accuracy, stability in the estimate, and consistency in the constraints, determines whether the refined transformation estimate is accepted as correct. Experimental results on a data set of 22 challenging image pairs show that the algorithm effectively aligns 19 of the 22 pairs and rejects 99.8 percent of the misalignments that occur when all possible pairs are tried. The algorithm substantially outperforms algorithms based on keypoint matching alone.