Results 1  10
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26
BRealtime nonrigid surface detection
 in Proc. IEEE Conf. Computer Vision Pattern Recognition
, 2005
"... We present a realtime method for detecting deformable surfaces, with no need whatsoever for a priori pose knowledge. Our method starts from a set of wide baseline point matches between an undeformed image of the object and the image in which it is to be detected. The matches are used not only to de ..."
Abstract

Cited by 51 (5 self)
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We present a realtime method for detecting deformable surfaces, with no need whatsoever for a priori pose knowledge. Our method starts from a set of wide baseline point matches between an undeformed image of the object and the image in which it is to be detected. The matches are used not only to detect but also to compute a precise mapping from one to the other. The algorithm is robust to large deformations, lighting changes, motion blur, and occlusions. It runs at 10 frames per second on a 2.8 GHz PC and we are not aware of any other published technique that produces similar results. Introducing deformable meshes, along with a well designed robust estimator, is the key to dealing with the large number of parameters involved in modeling deformable surfaces and rejecting erroneous matches for error rates of up to 95%, which is considerably more than what is required in practice. 1
Surface deformation models for nonrigid 3–d shape recovery. to appear
 IEEE Transactions on Pattern Analysis and Machine Intelligence
"... Abstract—Threedimensional detection and shape recovery of a nonrigid surface from video sequences require deformation models to effectively take advantage of potentially noisy image data. Here, we introduce an approach to creating such models for deformable 3D surfaces. We exploit the fact that the ..."
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Cited by 29 (5 self)
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Abstract—Threedimensional detection and shape recovery of a nonrigid surface from video sequences require deformation models to effectively take advantage of potentially noisy image data. Here, we introduce an approach to creating such models for deformable 3D surfaces. We exploit the fact that the shape of an inextensible triangulated mesh can be parameterized in terms of a small subset of the angles between its facets. We use this set of angles to create a representative set of potential shapes, which we feed to a simple dimensionality reduction technique to produce lowdimensional 3D deformation models. We show that these models can be used to accurately model a wide range of deforming 3D surfaces from video sequences acquired under realistic conditions. Index Terms—3D shape recovery, deformation model, nonrigid surfaces. 1
Convex optimization for deformable surface 3D tracking
 In Int. Conf. Computer Vision
, 2007
"... 3–D shape recovery of nonrigid surfaces from 3–D to 2–D correspondences is an underconstrained problem that requires prior knowledge of the possible deformations. Stateoftheart solutions involve enforcing smoothness constraints that limit their applicability and prevent the recovery of sharply ..."
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Cited by 18 (3 self)
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3–D shape recovery of nonrigid surfaces from 3–D to 2–D correspondences is an underconstrained problem that requires prior knowledge of the possible deformations. Stateoftheart solutions involve enforcing smoothness constraints that limit their applicability and prevent the recovery of sharply folding and creasing surfaces. Here, we propose a method that does not require such smoothness constraints. Instead, we represent surfaces as triangulated meshes and, assuming the pose in the first frame to be known, disallow large changes of edge orientation between consecutive frames, which is a generally applicable constraint when tracking surfaces in a 25 framespersecond video sequence. We will show that tracking under these constraints can be formulated as a Second Order Cone Programming feasibility problem. This yields a convex optimization problem with stable solutions for a wide range of surfaces with very different physical properties. 1.
Physically Valid Shape Parameterization for Monocular 3–D Deformable Surface Tracking
 In British Machine Vision Conference
, 2005
"... We develop a lowdimensional approximation of the set of possible deformations of smoothly deforming objects of planar topology. To this end, we propose a novel parameterization of inextensible surfaces that allows us first to effectively sample the space of all possible deformations, which is a pri ..."
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Cited by 17 (3 self)
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We develop a lowdimensional approximation of the set of possible deformations of smoothly deforming objects of planar topology. To this end, we propose a novel parameterization of inextensible surfaces that allows us first to effectively sample the space of all possible deformations, which is a priori very large, and then to derive the lowdimensional model using a simple dimensionality reduction technique. We incorporate the resulting models into a monocular tracking system that we use to capture complex deformations of objects such as sheets of paper or more flexible material. We also show that, even though the model was built by sampling the set of possible deformations of inextensible surfaces, it can also handle extensible ones. 1
A low dimensional fluid motion estimator
 Int. J. Comp. Vision
"... In this paper we propose a new motion estimator for image sequences depicting fluid flows. The proposed estimator is based on the Helmholtz decomposition of vector fields. This decomposition consists in representing the velocity field as a sum of a divergence free component and a vorticity free comp ..."
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Cited by 16 (8 self)
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In this paper we propose a new motion estimator for image sequences depicting fluid flows. The proposed estimator is based on the Helmholtz decomposition of vector fields. This decomposition consists in representing the velocity field as a sum of a divergence free component and a vorticity free component. The objective is to provide a lowdimensional parametric representation of optical flows by depicting them as deformations generated by a reduced number of vortex and source particles. Both components are approximated using a discretization of the vorticity and divergence maps through regularized Dirac measures. The resulting so called irrotational and solenoidal fields consist of linear combinations of basis functions obtained through a convolution product of the Green kernel gradient and the vorticity map or the divergence map respectively. The coefficient values and the basis function parameters are obtained by minimization of a functional relying on an integrated version of mass conservation principle of fluid mechanics. Results are provided on synthetic examples and real world sequences. 1
Progressive finite newton approach to realtime nonrigid surface detection
 In Proc. Conf. Computer Vision and Pattern Recognition
, 2007
"... Detecting nonrigid surfaces is an interesting research problem for computer vision and image analysis. One important challenge of nonrigid surface detection is how to register a nonrigid surface mesh having a large number of free deformation parameters. This is particularly significant for detecting ..."
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Cited by 11 (6 self)
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Detecting nonrigid surfaces is an interesting research problem for computer vision and image analysis. One important challenge of nonrigid surface detection is how to register a nonrigid surface mesh having a large number of free deformation parameters. This is particularly significant for detecting nonrigid surfaces from noisy observations. Nonrigid surface detection is usually regarded as a robust parameter estimation problem, which is typically solved iteratively from a good initialization in order to avoid local minima. In this paper, we propose a novel progressive finite Newton optimization scheme for the nonrigid surface detection problem, which is reduced to only solving a set of linear equations. The key of our approach is to formulate the nonrigid surface detection as an unconstrained quadratic optimization problem which has a closedform solution for a given set of observations. Moreover, we employ a progressive activeset selection scheme, which takes advantage of the rank information of detected correspondences. We have conducted extensive experiments for performance evaluation on various environments, whose promising results show that the proposed algorithm is more efficient and effective than the existing iterative methods. 1.
Deformable Surface Tracking Ambiguities
 In CVPR
, 2007
"... We study from a theoretical standpoint the ambiguities that occur when tracking a generic deformable surface under monocular perspective projection given 3–D to 2–D correspondences. We show that, additionally to the known scale ambiguity, a set of potential ambiguities can be clearly identified. Fro ..."
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Cited by 9 (4 self)
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We study from a theoretical standpoint the ambiguities that occur when tracking a generic deformable surface under monocular perspective projection given 3–D to 2–D correspondences. We show that, additionally to the known scale ambiguity, a set of potential ambiguities can be clearly identified. From this, we deduce a minimal set of constraints required to disambiguate the problem and incorporate them into a working algorithm that runs on real noisy data. 1.
Object recognition and segmentation by nonrigid quasidense matching
 In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008
, 2008
"... In this paper, we present a nonrigid quasidense matching method and its application to object recognition and segmentation. The matching method is based on the match propagation algorithm which is here extended by using local image gradients for adapting the propagation to smooth nonrigid deforma ..."
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Cited by 8 (0 self)
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In this paper, we present a nonrigid quasidense matching method and its application to object recognition and segmentation. The matching method is based on the match propagation algorithm which is here extended by using local image gradients for adapting the propagation to smooth nonrigid deformations of the imaged surfaces. The adaptation is based entirely on the local properties of the images and the method can be hence used in nonrigid image registration where global geometric constraints are not available. Our approach for object recognition and segmentation is directly built on the quasidense matching. The quasidense pixel matches between the model and test images are grouped into geometrically consistent groups using a method which utilizes the local affine transformation estimates obtained during the propagation. The number and quality of geometrically consistent matches is used as a recognition criterion and the location of the matching pixels directly provides the segmentation. The experiments demonstrate that our approach is able to deal with extensive background clutter, partial occlusion, large scale and viewpoint changes, and notable geometric deformations. 1.
M.R.: An effective approach to 3d deformable surface tracking
 In: Proceedings of the 10th European Conference on Computer Vision: Part III, ECCV ’08
, 2008
"... Abstract. The key challenge with 3D deformable surface tracking arises from the difficulty in estimating a large number of 3D shape parameters from noisy observations. A recent stateoftheart approach attacks this problem by formulating it as a Second Order Cone Programming (SOCP) feasibility prob ..."
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Cited by 8 (2 self)
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Abstract. The key challenge with 3D deformable surface tracking arises from the difficulty in estimating a large number of 3D shape parameters from noisy observations. A recent stateoftheart approach attacks this problem by formulating it as a Second Order Cone Programming (SOCP) feasibility problem. The main drawback of this solution is the high computational cost. In this paper, we first reformulate the problem into an unconstrained quadratic optimization problem. Instead of handling a large set of complicated SOCP constraints, our new formulation can be solved very efficiently by resolving a set of sparse linear equations. Based on the new framework, a robust iterative method is employed to handle large outliers. We have conducted an extensive set of experiments to evaluate the performance on both synthetic and realworld testbeds, from which the promising results show that the proposed algorithm not only achieves better tracking accuracy, but also executes significantly faster than the previous solution. 1
Nonlinear beam model for tracking large deformations
 In International Conference on Computer Vision
, 2007
"... In this paper we investigate physicsbased plane beam model, frequently used in mechanical and civil engineering, to track large nonlinear deformations in images. Such models do not only contribute to robust and precise tracking, in the presence of clutter and partial occlusions, but also allow to ..."
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Cited by 7 (0 self)
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In this paper we investigate physicsbased plane beam model, frequently used in mechanical and civil engineering, to track large nonlinear deformations in images. Such models do not only contribute to robust and precise tracking, in the presence of clutter and partial occlusions, but also allow to compute the forces that produce observed deformations. We verify the correctness of the recovered forces by using them in a simulation and compare the results to the original image displacements. We apply this method to track deformations of the pole vault, the rat whiskers and the car antenna. 1.