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22
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 519 (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.
Boundary Finding with Parametrically Deformable Models
, 1992
"... Introduction This work describes an approach to finding objects in images based on deformable shape models. Boundary finding in two and three dimensional images is enhanced both by considering the bounding contour or surface as a whole and by using modelbased shape information. Boundary finding u ..."
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Cited by 285 (6 self)
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Introduction This work describes an approach to finding objects in images based on deformable shape models. Boundary finding in two and three dimensional images is enhanced both by considering the bounding contour or surface as a whole and by using modelbased shape information. Boundary finding using only local information has often been frustrated by poorcontrast boundary regions due to occluding and occluded objects, adverse viewing conditions and noise. Imperfect image data can be augmented with the extrinsic information that a geometric shape model provides. In order to exploit modelbased information to the fullest extent, it should be incorporated explicitly, specifically, and early in the analysis. In addition, the bounding curve or surface can be profitably considered as a whole, rather than as curve or surface segments, because it tends to result in a more consistent solution overall. These models are best suited for objects whose diversity and irregularity of shape make
Using a Deformable Surface Model to Obtain a Shape Representation of the Cortex
 IEEE Trans. Med. Imag
, 1996
"... The problem of obtaining a mathematical representation of the cortex of the human brain is examined. A parametrization of the outer cortex is first obtained using a deformable surface algorithm which, motivated by the structure of the cortex, is constructed to find the central layer of thick surface ..."
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Cited by 93 (10 self)
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The problem of obtaining a mathematical representation of the cortex of the human brain is examined. A parametrization of the outer cortex is first obtained using a deformable surface algorithm which, motivated by the structure of the cortex, is constructed to find the central layer of thick surfaces. Based on this parametrization, a hierarchical representation of the cortical structure is proposed through its depth map and its curvature maps at various scales. Various experiments on magnetic resonance data are presented. I. Introduction The problem of finding and parametrizing boundaries in two and threedimensional images is often an important step toward shape visualization and analysis, and has been extensively studied in the image analysis and computer vision literature. Several methods have been proposed, basedboth on bottomup and topbottom procedures. One very promising model which combines robustness to noise and the flexibility to represent a broad class of shapes is base...
Partial Surface and Volume Matching in Three Dimensions
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... In this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this ..."
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Cited by 30 (1 self)
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In this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this
Reduction of BRL/CAD Models and Their Use in Automatic Target Recognition Algorithms
 In Proceedings: BRLCAD Symposium. Army Research Labs
, 1995
"... We are currently developing an Automatic Target Recognition (ATR) algorithm to locate an object using multisensor data. The ATR algorithm will determine corresponding points between a range (LADAR) image, a color (CCD) image, a thermal (FLIR) image and a BRL/CAD model of the object being located. Th ..."
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Cited by 10 (10 self)
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We are currently developing an Automatic Target Recognition (ATR) algorithm to locate an object using multisensor data. The ATR algorithm will determine corresponding points between a range (LADAR) image, a color (CCD) image, a thermal (FLIR) image and a BRL/CAD model of the object being located. The success of this process depends in part on which features can be automatically extracted from the model database. The BRL/CAD models we have for this process contain more detail than can be productively used by our ATR algorithm and must be reduced to a more appropriate form. This paper presents algorithms we are developing in order to reduce the BRL/CAD models a level of detail appropriate for the ATR algorithm. A secondary feature of these algorithms is to also maintain a parallel version with details sufficient to appear realistic when rendered. This rendering enables the ATR system to animate its search procedure for monitoring and debugging. Model reduction begins by converting the Co...
Partial Surface Matching by Using Directed Footprints
 In Proc. 12th Annual Symp. on Computational Geometry
, 1996
"... In this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this problem, we are given two objects in 3space, each represented as a set of points, scattered uniformly along its boundary or inside its volume. The goal is to find a rigid motio ..."
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Cited by 9 (0 self)
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In this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this problem, we are given two objects in 3space, each represented as a set of points, scattered uniformly along its boundary or inside its volume. The goal is to find a rigid motion of one object which makes a sufficiently large portion of its boundary lying sufficiently close to a corresponding portion of the boundary of the second object. This is an important problem in pattern recognition and in computer vision, with many industrial, medical, and chemical applications. Our algorithm is based on assigning a directed footprint to every point of the two sets, and locating all the pairs of points (one of each set) whose undirected components of the footprints are sufficiently similar. The algorithm then computes for each such pair of points all the rigid transformations that map the first point to the second, while making the respective direction components of ...
Obtaining 3D Silhouettes And Sampled Surfaces From Solid Models For Use In Computer Vision
, 1995
"... OF THESIS OBTAINING 3D SILHOUETTES AND SAMPLED SURFACES FROM SOLID MODELS FOR USE IN COMPUTER VISION Modelbased object recognition algorithms identify modeled objects in a scene by relating stored geometric models to features extracted from sensor data. This process can be combinatorially explosive ..."
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Cited by 8 (5 self)
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OF THESIS OBTAINING 3D SILHOUETTES AND SAMPLED SURFACES FROM SOLID MODELS FOR USE IN COMPUTER VISION Modelbased object recognition algorithms identify modeled objects in a scene by relating stored geometric models to features extracted from sensor data. This process can be combinatorially explosive as the amount of information presented to the recognition algorithm increases. This thesis presents a method for extracting only relevant features from a stored three dimensional (3D) model in an attempt to reduce the difficulty of the recognition process. The development of the methods presented here were driven by the needs of the Automatic Target Recognition (ATR) algorithm being developed concurrently at Colorado State University (CSU). The ATR algorithm locates an object using multisensor data by determining the correspondence between a range (LADAR) image, a color image, a thermal (FLIR) image, and a Computer Aided Design (CAD) geometric model. The success of this process depends in ...
On Bending Invariant Signatures for Surfaces
, 2003
"... Isometric surfaces share the same geometric structure, also known as the “first fundamental form.” For example, all possible bendings of a given surface that includes all length preserving deformations without tearing or stretching the surface are considered to be isometric. We present a method to ..."
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Cited by 5 (0 self)
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Isometric surfaces share the same geometric structure, also known as the “first fundamental form.” For example, all possible bendings of a given surface that includes all length preserving deformations without tearing or stretching the surface are considered to be isometric. We present a method to construct a bending invariant signature for such surfaces. This invariant representation is an embedding of the geometric structure of the surface in a small dimensional Euclidean space in which geodesic distances are approximated by Euclidean ones. The bending invariant representation is constructed by first measuring the intergeodesic distances between uniformly distributed points on the surface. Next, a multidimensional scaling (MDS) technique is applied to extract coordinates in a finite dimensional Euclidean space in which geodesic distances are replaced by Euclidean ones. Applying this transform to various surfaces with similar geodesic structures (first fundamental form) maps them into similar signature surfaces. We thereby translate the problem of matching nonrigid objects in various postures into a simpler problem of matching rigid objects. As an example, we show a simple surface classification method that uses our bending invariant signatures.
Learning Relational Structures: Applications in Computer Vision
 Applied Intelligence
, 1994
"... We present and compare two new techniques for Learning Relational Structures (RS) as they occur in 2D pattern and 3D object recognition. These techniques, EvidenceBased Networks (EBSNNet) and Rulegraphs (RG) combine techniques from Computer Vision with those from Machine Learning and Graph Matchin ..."
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Cited by 3 (2 self)
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We present and compare two new techniques for Learning Relational Structures (RS) as they occur in 2D pattern and 3D object recognition. These techniques, EvidenceBased Networks (EBSNNet) and Rulegraphs (RG) combine techniques from Computer Vision with those from Machine Learning and Graph Matching. The EBSNNet has the ability to generalize pattern rules from training instances in terms of bounds on both unary (single part) and binary (part relation) numerical features. It also learns, the compatibilities between unary and binary feature states in defining different pattern classes. Rulegraphs check this compatibilitybetween unary and binary rules bycombining Evidence Theory with Graph Theory. The two systems are tested and compared using a number of different pattern and object recognition problems. 2 1 Introduction In the context of Computer Vision, Relational Structures (RS) refer to the representation of patterns or shapes in terms of attributes of parts and part relations (s...
Biometrics with EigenHand
 in Industrial Electronics and Applications, 2006 1ST IEEE Conference on 2426 May 2006 Page(s):1 – 4
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