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56
On the Sensitivity of the Hough Transform for Object Recognition
 IEEE TRANS. ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1990
"... Object recognition from sensory data involves, in part, determining the pose of a model with respect to a scene. A common method for finding an object's pose is the generalized Hough transform, which accumulates evidence for possible coordinate transformations in a parameter space whose axes ar ..."
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Cited by 108 (5 self)
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Object recognition from sensory data involves, in part, determining the pose of a model with respect to a scene. A common method for finding an object's pose is the generalized Hough transform, which accumulates evidence for possible coordinate transformations in a parameter space whose axes are the quantized transformation parameters. Large clusters of similar transformations in that space are taken as evidence of a correct match. In this article, we provide a theoretical analysis of the behavior of such methods. We derive bounds on the set of transformations consistent with each pairing of data and model features, in the presence of noise and occlusion in the image. We also provide bounds on the likelihood of false peaks in the parameter space, as a function of noise, occlusion, and tessellation effects. We argue that blithely applying such methods to complex recognition tasks is a risky proposition, as the probability of false positives can be very high.
A Fast Flexible Docking Method using an Incremental Construction Algorithm
 J. Mol. Biol
, 1996
"... We present an automatic method for docking organic ligands into protein Center for Information binding sites. The method can be used in the design process of specific Technology (GMD), Institute protein ligands. It combines an appropriate model of the physicochemical for Algorithms and Scientific p ..."
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Cited by 106 (3 self)
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We present an automatic method for docking organic ligands into protein Center for Information binding sites. The method can be used in the design process of specific Technology (GMD), Institute protein ligands. It combines an appropriate model of the physicochemical for Algorithms and Scientific properties of the docked molecules with efficient methods for sampling the Computing (SCAI), Schloß conformational space of the ligand. If the ligand is flexible, it can adopt
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 81 (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...
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 (...
Dressed Human Modeling, Detection, and Parts Localization
, 2001
"... This dissertation presents an integrated human shape modeling, detection, and body part localization vision system. It demonstrates that the system can (1) detect pedestrians in various shapes, sizes, postures, partial occlusion, and clothing from a moving vehicle using stereo cameras; (2) locate th ..."
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Cited by 23 (1 self)
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This dissertation presents an integrated human shape modeling, detection, and body part localization vision system. It demonstrates that the system can (1) detect pedestrians in various shapes, sizes, postures, partial occlusion, and clothing from a moving vehicle using stereo cameras; (2) locate the joints of a person automatically and accurately without employing any markers around the joints.
Recognizing 3D Objects from 2D Images: An Error Analysis
, 1992
"... Many recent object recognition systems use a small number of pairings of data and model features to compute the 3D transformation from a model coordinate frame into the sensor coordinate system. In the case of perfect image data, these systems seem to work well. With uncertain image data, however, t ..."
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Cited by 22 (3 self)
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Many recent object recognition systems use a small number of pairings of data and model features to compute the 3D transformation from a model coordinate frame into the sensor coordinate system. In the case of perfect image data, these systems seem to work well. With uncertain image data, however, the performance of such methods is less well understood. In this paper, we examine the effects of twodimensional sensor uncertainty on the computation of threedimensional model transformations. We use this analysis to bound the uncertainty in the transformation parameters, as well as the uncertainty associated with applying the transformation to map other model features into the image. We also examine the effects of the transformation uncertainty on the effectiveness of recognition methods.
Exact and Approximate Solutions of the PerspectiveThreePoint Problem
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1992
"... Modelbased pose estimation techniques that match image and model triangles rquire large numbers of matching operations in realworld applications. We show that by using approximations to perspective, 2D lookup tables can be built for each of the triangles of the models. An approximation called &quo ..."
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Cited by 20 (0 self)
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Modelbased pose estimation techniques that match image and model triangles rquire large numbers of matching operations in realworld applications. We show that by using approximations to perspective, 2D lookup tables can be built for each of the triangles of the models. An approximation called "weak perspective" has been applied previously to this problem; we consider two other perspective approximations: paraperspective and orthoperspective. These approximations produce lower errors for offcenter image features than weak perspective.
Geometric sensing of known planar shapes
 In Proceedings of the 1996 IEEE International Conference on Robotics and Automation
, 1996
"... Industrial assembly involves sensing the pose (orientation and position) of a part. Efficient and reliable sensing strategies can be developed for an assembly task if the shape of the part is known in advance. In this article we investigate two problems of determining the pose of a polygonal part of ..."
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Cited by 19 (10 self)
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Industrial assembly involves sensing the pose (orientation and position) of a part. Efficient and reliable sensing strategies can be developed for an assembly task if the shape of the part is known in advance. In this article we investigate two problems of determining the pose of a polygonal part of known shape for the cases of a continuum and a finite number of possible poses respectively. The first problem, named sensing by inscription, involves determining the pose of a convex ngon from a set of m supporting cones. An algorithm with running time O(nm) that almost always reduces to O(n+m log n) is presented to solve for all possible poses of the polygon. We prove that the number of possible poses cannot exceed 6n, given m ≥ 2 supporting cones with distinct vertices. Simulation experiments demonstrate that two supporting cones are sufficient to determine the real pose of the ngon in most cases. Our results imply that sensing in practice can be carried out by obtaining viewing angles of a planar part at multiple exterior sites in the plane. On many occasions a parts feeder will have reduced the number of possible poses of a part to a small finite set. Our second problem, named sensing by point sampling,
Time and Space Efficient Pose Clustering
 In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
, 1994
"... This paper shows that the pose clustering method of object recognition can be decomposed into small subproblems without loss of accuracy. Randomization can then be used to limit the number of subproblems that need to be examined to achieve accurate recognition. These techniques are used to decrease ..."
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Cited by 15 (6 self)
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This paper shows that the pose clustering method of object recognition can be decomposed into small subproblems without loss of accuracy. Randomization can then be used to limit the number of subproblems that need to be examined to achieve accurate recognition. These techniques are used to decrease the computational complexity of pose clustering. The clustering step is formulated as an efficient tree search of the pose space. This method requires little memory since not many poses are clustered at a time. Analysis shows that pose clustering is not inherently more sensitive to noise than other methods of generating hypotheses. Finally, experiments on real and synthetic data are presented. 1 Introduction Modelbased object recognition systems determine which objects appear in images using a catalog of object models and estimate their positions and orientations (poses) relative to the camera. This paper examines methods of improving the efficiency of the pose clustering method of object ...
Improving the Generalized Hough Transform Through Imperfect Grouping
 Image and Vision Computing
, 1998
"... paper analyses the improvements that can be gained in the generalized Hough transform method for recognizing objects through the use of imperfect perceptual grouping techniques. In particular, we consider simple grouping techniques that determine pairs of points that are likely to belong to the same ..."
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Cited by 15 (4 self)
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paper analyses the improvements that can be gained in the generalized Hough transform method for recognizing objects through the use of imperfect perceptual grouping techniques. In particular, we consider simple grouping techniques that determine pairs of points that are likely to belong to the same object using a criterion based on connectedness in the image edge map. It is shown that such imperfect grouping techniques can considerably improve both the efficiency and accuracy of object recognition. Experiments are described that demonstrate the