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A Survey of Medical Image Registration
, 1998
"... The purpose of this chapter is to present a survey of recent publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods The statistics of t ..."
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Cited by 405 (3 self)
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The purpose of this chapter is to present a survey of recent publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods The statistics of the classification show definite trends in the evolving registration techniques, which will be discussed. At this moment, the bulk of interesting intrinsic methods is either based on segmented points or surfaces, or on techniques endeavoring to use the full information content of the images involved. Keywords: registration, matching Received May 25, 1997
Using spin images for efficient object recognition in cluttered 3D scenes
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1999
"... We present a 3D shapebased object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spinimage representation. The spinimage is a data level shape descriptor that i ..."
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Cited by 376 (9 self)
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We present a 3D shapebased object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spinimage representation. The spinimage is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spinimages that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes. This research was performed at Carnegie Mellon University and was supported by the US Department Surface matching is a technique from 3D computer vision that has many applications in the area of robotics and automation. Through surface matching, an object can be recognized in a scene by comparing a sensed surface to an object surface stored in memory. When the object surface is matched to the scene surface, an association is made between something known (the object) and
A survey of freeform object representation and recognition techniques
 Computer Vision and Image Understanding
, 2001
"... Advances in computer speed, memory capacity, and hardware graphics acceleration have made the interactive manipulation and visualization of complex, detailed (and therefore large) threedimensional models feasible. These models are either painstakingly designed through an elaborate CAD process or re ..."
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Cited by 150 (1 self)
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Advances in computer speed, memory capacity, and hardware graphics acceleration have made the interactive manipulation and visualization of complex, detailed (and therefore large) threedimensional models feasible. These models are either painstakingly designed through an elaborate CAD process or reverse engineered from sculpted prototypes using modern scanning technologies and integration methods. The availability of detailed data describing the shape of an object offers the computer vision practitioner new ways to recognize and localize freeform objects. This survey reviews recent literature on both the 3D model building process and techniques used to match and identify freeform objects from imagery. c ○ 2001 Academic Press 1.
ICP Registration using Invariant Features
, 2002
"... This paper investigates the use of Euclidean invariant features in a generalization of iterative closest point registration of range images. Pointwise correspondences are chosen as the closest point with respect to a weighted linear combination of positional and feature distances. It is shown that u ..."
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Cited by 90 (0 self)
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This paper investigates the use of Euclidean invariant features in a generalization of iterative closest point registration of range images. Pointwise correspondences are chosen as the closest point with respect to a weighted linear combination of positional and feature distances. It is shown that under ideal noisefree conditions, correspondences formed using this distance function are correct more often than correspondences formed using the positional distance alone. In addition, monotonic convergence to at least a local minimum is shown to hold for this method. When noise is present, a method that automatically sets the optimal relative contribution of features and positions is described. This method trades off error in feature values due to noise against error in positions due to misalignment. Experimental results suggest that using invariant features decreases the probability of being trapped in a local minimum, and may be an effective solution for difficult range image registration problems where the scene is very small compared to the model.
RANSACbased DARCES: A New Approach for Fast Automatic Registration of Partially Overlapping Range Images
"... Registration of two partiallyoverlapping range images taken from different views is an important task in 3D computer vision. In general, if there is no initial knowledge about the poses of these two views, the information used for solving the 3D registration problem is mainly the 3D shape of the co ..."
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Cited by 71 (3 self)
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Registration of two partiallyoverlapping range images taken from different views is an important task in 3D computer vision. In general, if there is no initial knowledge about the poses of these two views, the information used for solving the 3D registration problem is mainly the 3D shape of the common parts of the two partiallyoverlapping data sets.
Least squares 3D surface and curve matching
 ISPRS Journal of Photogrammetry and Remote Sensing
, 2005
"... The automatic coregistration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. This multiple registration problem can be defined as a surface matching task. We treat it as least squares matching of overlapping surfaces. The surface may have been digitized/sampled poin ..."
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Cited by 60 (13 self)
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The automatic coregistration of point clouds, representing 3D surfaces, is a relevant problem in 3D modeling. This multiple registration problem can be defined as a surface matching task. We treat it as least squares matching of overlapping surfaces. The surface may have been digitized/sampled point by point using a laser scanner device, a photogrammetric method or other surface measurement techniques. Our proposed method estimates the transformation parameters of one or more 3D search surfaces with respect to a 3D template surface, using the Generalized GaussMarkoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulation gives the opportunity of matching arbitrarily oriented 3D surface patches. It fully considers 3D geometry. Besides the mathematical model and execution aspects we address the further extensions of the basic model. We also show how this method can be used for curve matching in 3D space and matching of curves to surfaces. Some practical examples based on the registration of closerange laser scanner and photogrammetric point clouds are presented for the demonstration of the method. This surface matching technique is a generalization of the least squares image matching concept and offers high flexibility for any kind of 3D surface correspondence problem, as well as statistical tools for the analysis of the quality of final matching results.
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.
Surface Registration by Matching Oriented Points
, 1997
"... For registration of 3D freeform surfaces we have developed a representation which requires no knowledge of the transformation between views. The representation comprises descriptive images associated with oriented points on the surface of an object. Constructed using single point bases, these imag ..."
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Cited by 55 (6 self)
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For registration of 3D freeform surfaces we have developed a representation which requires no knowledge of the transformation between views. The representation comprises descriptive images associated with oriented points on the surface of an object. Constructed using single point bases, these images are data level shape descriptions that are used for efficient matching of oriented points. Correlation of images is used to establish point correspondences between two views; from these correspondences a rigid transformation that aligns the views is calculated. The transformation is then refined and verified using a modified iterative closest point algorithm. To demonstrate the generality of our approach, we present results from multiple sensing domains. 1. Introduction Surface registration is the process that aligns 3D data sets acquired from different view points or at different times. A common application of surface registration is to spatially reconcile multiple views of a scene in...
Registering Two Overlapping Range Images
, 1999
"... This paper describes a method of automatically performing the registration of two range images that have signi#cant overlap. We #rst #nd points of interest in the intensity data that comes with each range image. Then we perform a triangulation of the 3D range points associated with these 2D interest ..."
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Cited by 41 (0 self)
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This paper describes a method of automatically performing the registration of two range images that have signi#cant overlap. We #rst #nd points of interest in the intensity data that comes with each range image. Then we perform a triangulation of the 3D range points associated with these 2D interest points. All possible pairs of triangles between the two 3D triangulations are then matched. The fact that we have 3D data available makes it possible to e#ciently prune matches. We do this pruning by using a simple and e#ective set of compatibility tests between potentially matching triangles and vertices. The best match is the one that aligns the largest number of interest points between the two range images. The algorithms are demonstrated experimentally on a number of di#erent range image pairs.
Surface Matching for Object Recognition in Complex 3D Scenes
 Image and Vision Computing
, 1998
"... We present an approach to recognition of complex objects in cluttered 3D scenes that does not require feature extraction or segmentation. Our object representation comprises descriptive images associated with oriented points on the surface of an object. Using a single point basis constructed from a ..."
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Cited by 39 (1 self)
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We present an approach to recognition of complex objects in cluttered 3D scenes that does not require feature extraction or segmentation. Our object representation comprises descriptive images associated with oriented points on the surface of an object. Using a single point basis constructed from an oriented point, the position of other points on the surface of the object can be described by two parameters. The accumulation of these parameters for many points on the surface of the object results in an image at each oriented point. These images, localized descriptions of the global shape of the object, are invariant to rigid transformations. Through correlation of images, point correspondences between a model and scene data are established. Geometric consistency is used to group the correspondences from which plausible rigid transformations that align the model with the scene are calculated. The transformations are then refined and verified using a modified iterative closest point algo...