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63
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.
Shape Matching: Similarity Measures and Algorithms
, 2001
"... Shape matching is an important ingredient in shape retrieval, recognition and classification, alignment and registration, and approximation and simplification. This paper treats various aspects that are needed to solve shape matching problems: choosing the precise problem, selecting the properties o ..."
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Cited by 91 (1 self)
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Shape matching is an important ingredient in shape retrieval, recognition and classification, alignment and registration, and approximation and simplification. This paper treats various aspects that are needed to solve shape matching problems: choosing the precise problem, selecting the properties of the similarity measure that are needed for the problem, choosing the specific similarity measure, and constructing the algorithm to compute the similarity. The focus is on methods that lie close to the field of computational geometry.
New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence
"... A fundamental open problem in computer visiondetermining pose and correspondence between two sets of points in spaceis solved with a novel, fast [O(nm)], robust and easily implementable algorithm. The technique works on noisy 2D or 3D point sets that may be of unequal sizes and may differ by n ..."
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Cited by 85 (19 self)
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A fundamental open problem in computer visiondetermining pose and correspondence between two sets of points in spaceis solved with a novel, fast [O(nm)], robust and easily implementable algorithm. The technique works on noisy 2D or 3D point sets that may be of unequal sizes and may differ by nonrigid transformations. Using a combination of optimization techniques such as deterministic annealing and the softassign, which have recently emerged out of the recurrent neural network/statistical physics framework, analog objective functions describing the problems are minimized. Over thirty thousand experiments, on randomly generated points sets with varying amounts of noise and missing and spurious points, and on handwritten character sets demonstrate the robustness of the algorithm. Keywords: Pointmatching, pose estimation, correspondence, neural networks, optimization, softassign, deterministic annealing, affine. 1 Introduction Matching the representations of two images has long...
Parallel Algorithms for Hierarchical Clustering
 Parallel Computing
, 1995
"... Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n 2 ) algorithms are known for this problem [3, 4, 10, 18]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms f ..."
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Cited by 80 (1 self)
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Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n 2 ) algorithms are known for this problem [3, 4, 10, 18]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms for hierarchical clustering. Parallel algorithms to perform hierarchical clustering using several distance metrics are then described. Optimal PRAM algorithms using n log n processors are given for the average link, complete link, centroid, median, and minimum variance metrics. Optimal butterfly and tree algorithms using n log n processors are given for the centroid, median, and minimum variance metrics. Optimal asymptotic speedups are achieved for the best practical algorithm to perform clustering using the single link metric on a n log n processor PRAM, butterfly, or tree. Keywords. Hierarchical clustering, pattern analysis, parallel algorithm, butterfly network, PRAM algorithm. 1 In...
On the Verification of Hypothesized Matches in ModelBased Recognition
, 1989
"... ... In this paper we present a more rigorous approach in which the conditions under which to accept a match are derived based on fundamental grounds. We obtain an expression that relates the probability of a match occurring at random to the fraction of model features that are accounted for by the ma ..."
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Cited by 65 (1 self)
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... In this paper we present a more rigorous approach in which the conditions under which to accept a match are derived based on fundamental grounds. We obtain an expression that relates the probability of a match occurring at random to the fraction of model features that are accounted for by the match. This expression is a function of the number of model features, the number of image features, and a bound on the degree of sensor noise. One
Planar Object Recognition using Projective Shape Representation
 International Journal of Computer Vision
, 1995
"... We describe a model based recognition system, called LEWIS, for the identification of planar objects based on a projectively invariant representation of shape. The advantages of this shape description include simple model acquisition (direct from images), no need for camera calibration or object pos ..."
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Cited by 51 (9 self)
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We describe a model based recognition system, called LEWIS, for the identification of planar objects based on a projectively invariant representation of shape. The advantages of this shape description include simple model acquisition (direct from images), no need for camera calibration or object pose computation, and the use of index functions. We describe the feature construction and recognition algorithms in detail and provide an analysis of the combinatorial advantages of using index functions. Index functions are used to select models from a model base and are constructed from projective invariants based on algebraic curves and a canonical projective coordinate frame. Examples are given of object recognition from images of real scenes, with extensive object libraries. Successful recognition is demonstrated despite partial occlusion by unmodelled objects, and realistic lighting conditions. 1 Introduction 1.1 Overview In the context of this paper, recognition is defined as the prob...
Viewpointindependent object class detection using 3d feature maps
 in In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
, 2008
"... This paper presents a 3D approach to multiview object class detection. Most existing approaches recognize object classes for a particular viewpoint or combine classifiers for a few discrete views. We propose instead to build 3D representations of object classes which allow to handle viewpoint chang ..."
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Cited by 49 (1 self)
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This paper presents a 3D approach to multiview object class detection. Most existing approaches recognize object classes for a particular viewpoint or combine classifiers for a few discrete views. We propose instead to build 3D representations of object classes which allow to handle viewpoint changes and intraclass variability. Our approach extracts a set of pose and class discriminant features from synthetic 3D object models using a filtering procedure, evaluates their suitability for matching to real image data and represents them by their appearance and 3D position. We term these representations 3D Feature Maps. For recognizing an object class in an image we match the synthetic descriptors to the real ones in a 3D voting scheme. Geometric coherence is reinforced by means of a robust pose estimation which yields a 3D bounding box in addition to the 2D localization. The precision of the 3D pose estimation is evaluated on a set of images of a calibrated scene. The 2D localization is evaluated on the PASCAL 2006 dataset for motorbikes and cars, showing that its performance can compete with stateoftheart 2D object detectors. 1.
A robot vision system for recognizing 3D objects in loworder polynomial time
 IEEE Trans. Syst., Man, Cybern
, 1989
"... AhsrrucrThe two factors that determine the time complexity associated with modeldriven interpretation of range maps are: 1) the particular strategy used for the generation of object hypotheses; and 2) the manner in which both the model and the sensed data are organized, data organization being a p ..."
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Cited by 46 (6 self)
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AhsrrucrThe two factors that determine the time complexity associated with modeldriven interpretation of range maps are: 1) the particular strategy used for the generation of object hypotheses; and 2) the manner in which both the model and the sensed data are organized, data organization being a primary determinant of the efficiency of verification of a given hypothesis. 3DPOLY, a working system for recognizing objects in the presence of occlusion and against cluttered backgrounds is presented. The time complexity of this system is only O ( n *) for single object recognition, where 17 is the number of features on the object. The most novel aspect of this system is the manner in which the feature data are organized for the models; we use a data structure called the feature sphere for the purpose. Efficient constant time algorithms for assigning a feature to its proper place on a feature sphere and for extracting the neighbors of a given feature from the feature sphere representation are present. For hypothesis generation, we use local feature sets, a notion similar to those used before us by Rolles, Shirai and others. The combination of the feature sphere idea for streamlining verification and the local feature sets for hypothesis generation results in a system whose time complexity has a loworder polynomial bound. I.
A Robust Point Matching Algorithm for Autoradiograph Alignment
, 1997
"... We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the onetoone correspondences (or homologies) between point features extracted from the images and rejecting nonhomologies as outliers. In this way, we atte ..."
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Cited by 38 (12 self)
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We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the onetoone correspondences (or homologies) between point features extracted from the images and rejecting nonhomologies as outliers. In this way, we attempt to account for the local natural and artifactual differences between the autoradiograph slices. We have executed the resulting automated algorithm on a set of left prefrontal cortex autoradiograph slices, specifically demonstrated its ability to perform point outlier rejection, validated it using synthetically generated spatial mappings and provided a visual comparison against the well known iterated closest point (ICP) algorithm. Visualization of a stack of aligned left prefrontal cortex autoradiograph slices is also provided.
Optimal Geometric Model Matching Under Full 3D Perspective
, 1994
"... Modelbased object recognition systems have rarely dealt directly with 3D perspective while matching models to images. The algorithms presented here use 3D pose recovery during matching to explicitly and quantitatively account for changes in model appearance associated with 3D perspective. These alg ..."
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Cited by 31 (14 self)
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Modelbased object recognition systems have rarely dealt directly with 3D perspective while matching models to images. The algorithms presented here use 3D pose recovery during matching to explicitly and quantitatively account for changes in model appearance associated with 3D perspective. These algorithms use randomstart local search to find, with high probability, the globally optimal correspondence between model and image features in spaces containing over 2 100 possible matches. Three specific algorithms are compared on robot landmark recognition problems. A fullperspective algorithm uses the 3D pose algorithm in all stages of search while two hybrid algorithms use a computationally less demanding weakperspective procedure to rank alternative matches and updates 3D pose only when moving to a new match. These hybrids successfully solve problems involving perspective, and in less time than required by the fullperspective algorithm.