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39
Articulated Shape Matching Using Laplacian Eigenfunctions and Unsupervised Point Registration
"... Matching articulated shapes represented by voxelsets reduces to maximal subgraph isomorphism when each set is described by a weighted graph. Spectral graph theory can be used to map these graphs onto lower dimensional spaces and match shapes by aligning their embeddings in virtue of their invarian ..."
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Cited by 47 (11 self)
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Matching articulated shapes represented by voxelsets reduces to maximal subgraph isomorphism when each set is described by a weighted graph. Spectral graph theory can be used to map these graphs onto lower dimensional spaces and match shapes by aligning their embeddings in virtue of their invariance to change of pose. Classical graph isomorphism schemes relying on the ordering of the eigenvalues to align the eigenspaces fail when handling large datasets or noisy data. We derive a new formulation that finds the best alignment between two congruent Kdimensional sets of points by selecting the best subset of eigenfunctions of the Laplacian matrix. The selection is done by matching eigenfunction signatures built with histograms, and the retained set provides a smart initialization for the alignment problem with a considerable impact on the overall performance. Dense shape matching casted into graph matching reduces then, to point registration of embeddings under orthogonal transformations; the registration is solved using the framework of unsupervised clustering and the EM algorithm. Maximal subset matching of non identical shapes is handled by defining an appropriate outlier class. Experimental results on challenging examples show how the algorithm naturally treats changes of topology, shape variations and different sampling densities. 1.
Novel skeletal representation for articulated creatures
 In Proc. European Conf. on Computer Vision
, 2004
"... Abstract. Volumetric structures are frequently used as shape descriptors for 3D data. The capture of such data is being facilitated by developments in multiview video and range scanning, extending to subjects that are alive and moving. In this paper, we examine visionbased modeling and the related ..."
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Cited by 28 (1 self)
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Abstract. Volumetric structures are frequently used as shape descriptors for 3D data. The capture of such data is being facilitated by developments in multiview video and range scanning, extending to subjects that are alive and moving. In this paper, we examine visionbased modeling and the related representation of moving articulated creatures using spines. We define a spine as a branching axial structure representing the shape and topology of a 3D object’s limbs, and capturing the limbs’ correspondence and motion over time. Our spine concept builds on skeletal representations often used to describe the internal structure of an articulated object and the significant protrusions. The algorithms for determining both 2D and 3D skeletons generally use an objective function tuned to balance stability against the responsiveness to detail. Our representation of a spine provides for enhancements over a 3D skeleton, afforded by temporal robustness and correspondence. We also introduce a probabilistic framework that is needed to compute the spine from a sequence of surface data. We present a practical implementation that approximates the spine’s joint probability function to reconstruct spines for synthetic and real subjects that move.
3d skeletonbased body pose recovery
 In: Proceedings of the 3rd International Symposium on 3D Data Processing, Visualization and Transmission, Chapel Hill (USA
, 2006
"... This paper presents an approach to recover body motions from multiple views using a 3D skeletal model. It takes, as input, foreground silhouette sequences from multiple viewpoints, and computes, for each frame, the skeleton pose which best fit the body pose. Skeletal models encode mostly motion info ..."
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Cited by 20 (0 self)
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This paper presents an approach to recover body motions from multiple views using a 3D skeletal model. It takes, as input, foreground silhouette sequences from multiple viewpoints, and computes, for each frame, the skeleton pose which best fit the body pose. Skeletal models encode mostly motion information and allows therefore to separate motion estimation from shape estimation for which solutions exist; And focusing on motion parameters significantly reduces the dependancy on specific body shapes, yielding thus more flexible solutions for body motion capture. However, a problem generally faced with skeletal models is to find adequate measurements with which to fit the model. In this paper, we propose to use the medial axis of the body shape to this purpose. Such medial axis can be estimated from the visual hull, a shape approximation which is easily obtained from the silhouette information. Experiments show that this approach is robust to several perturbations in the model or in the input data, and also allows fast body motions or, equivalently, important motions between consecutive frames. 1.
Learning Kinematic Models for Articulated Objects
"... Topic: estimation, prediction Oral presentation or poster presentation Home environments are envisioned as one of the key application areas for service robots. Robots operating in such environments are typically faced with a variety objects they have to deal with or to manipulate to fulfill a given ..."
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Cited by 17 (8 self)
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Topic: estimation, prediction Oral presentation or poster presentation Home environments are envisioned as one of the key application areas for service robots. Robots operating in such environments are typically faced with a variety objects they have to deal with or to manipulate to fulfill a given task. Many objects are not rigid since they have moving parts such as drawers or doors. Understanding the spatial movements of parts of such objects is essential for service robots to allow them to plan relevant actions such as dooropening trajectories. Ideally, robots are able to autonomously infer these articulation models by observation. In this work, we therefore investigate the problem of learning kinematic models of articulated objects from observations. As an illustrating example, consider the left three images of Figure 1 which depict two examples for observations of the door of a microwave oven and a learned, onedimensional description of the door motion. Our problem can be formulated as follows: Given a sequence of rigid body poses from observed objects parts, learn a compact kinematic model describing the whole articulated object. This kinematic model has to define (i) which parts are connected, (ii) the dimensionality of the latent (not observed) actuation space of the object, and (iii) a kinematic function between different body parts in a generative way allowing a robot
Multicamera tracking of articulated human motion using motion and shape cues
 IN ASIAN CONFERENCE ON COMPUTERVISION
, 2006
"... We present a framework and algorithm for tracking articulated motion for humans. We use multiple calibrated cameras and an articulated human shape model. Tracking is performed using motion cues as well as imagebased cues (such as silhouettes and “motion residues” hereafter referred to as spatial c ..."
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Cited by 10 (3 self)
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We present a framework and algorithm for tracking articulated motion for humans. We use multiple calibrated cameras and an articulated human shape model. Tracking is performed using motion cues as well as imagebased cues (such as silhouettes and “motion residues” hereafter referred to as spatial cues,) as opposed to constructing a 3D volume image or visual hulls. Our algorithm consists of a predictor and corrector: the predictor estimates the pose at the t + 1 using motion information between images at t and t + 1. The error in the estimated pose is then corrected using spatial cues from images at t + 1. In our predictor, we use robust multiscale parametric optimisation to estimate the pixel displacement for each body segment. We then use an iterative procedure to estimate the change in pose from the pixel displacement of points on the individual body segments. We present a method for fusing information from different spatial cues such as silhouettes and “motion residues” into a single energy function. We then express this energy function in terms of the pose parameters, and find the optimum pose for which the energy is minimised.
Segmentation and probabilistic registration of articulated body models
 In Proc. Int’l Conf. Pattern Recognition
, 2006
"... There are different approaches to pose estimation and registration of different body parts using voxel data. We propose a general bottomup approach in order to segment the voxels into different body parts. The voxels are first transformed into a high dimensional space which is the eigenspace of the ..."
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Cited by 6 (2 self)
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There are different approaches to pose estimation and registration of different body parts using voxel data. We propose a general bottomup approach in order to segment the voxels into different body parts. The voxels are first transformed into a high dimensional space which is the eigenspace of the Laplacian of the neighbourhood graph. We exploit the properties of this transformation and fit splines to the voxels belonging to different body segments in eigenspace. The boundary of the splines is determined by examination of the error in spline fitting. We then use a probabilistic approach to register the segmented body segments by utilizing their connectivity and prior knowledge of the general structure of the subjects. We present results on real data, containing both simple and complex poses. While we use human subjects in our experiment, the method is fairly general and can be applied to voxelbased registration of any articulated or nonrigid object composed of primarily 1D parts. 1.
Coherent Laplacian 3D protrusion segmentation
"... In this paper, an analysis of locally linear embedding (LLE) in the context of clustering is developed. As LLE conserves the local affine coordinates of points, shape protrusions as highcurvature regions of the surface are preserved. Also, LLE’s covariance constraint acts as a force stretching thos ..."
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Cited by 5 (1 self)
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In this paper, an analysis of locally linear embedding (LLE) in the context of clustering is developed. As LLE conserves the local affine coordinates of points, shape protrusions as highcurvature regions of the surface are preserved. Also, LLE’s covariance constraint acts as a force stretching those protrusions and making them wider separated and lower dimensional. A novel scheme for unsupervised bodypart segmentation along time sequences is thus proposed in which 3D shapes are clustered after embedding. Clusters are propagated in time, and merged or split in an unsupervised fashion to accommodate changes of the body topology. Comparisons on synthetic, and real data with ground truth, are run with direct segmentation in 3D by EM clustering and ISOMAPbased clustering. Robustness and the effects of topology transitions are discussed. 1.
Articulated Shape Matching Using Locally Linear Embedding and Orthogonal Alignment
"... In this paper we propose a method for matching articulated shapes represented as large sets of 3D points by aligning the corresponding embedded clouds generated by locally linear embedding. In particular we show that the problem is equivalent to aligning two sets of points under an orthogonal transf ..."
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Cited by 5 (0 self)
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In this paper we propose a method for matching articulated shapes represented as large sets of 3D points by aligning the corresponding embedded clouds generated by locally linear embedding. In particular we show that the problem is equivalent to aligning two sets of points under an orthogonal transformation acting onto the ddimensional embeddings. The method may well be viewed as belonging to the modelbased clustering framework and is implemented as an EM algorithm that alternates between the estimation of correspondences between datapoints and the estimation of an optimal alignment transformation. Correspondences are initialized by embedding one set of datapoints onto the other one through outofsample extension. Results for pairs of voxelsets representing moving persons are presented. Empirical evidence on the influence of the dimension of the embedding space is provided, suggesting that working with higherdimensional spaces helps matching in challenging realworld scenarios, without collateral effects on the convergence. 1.
Acquisition of Articulated Human Body Models using Multiple Cameras
 In IV Conference on Articulated Motion and Deformable Objects, Andratx, Mallorca
, 2006
"... Abstract. Motion capture is an important application in different areas such as biomechanics, computer animation, and humancomputer interaction. Current motion capture methods typically use human body models in order to guide pose estimation and tracking. We model the human body as a set of tapered ..."
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Cited by 5 (4 self)
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Abstract. Motion capture is an important application in different areas such as biomechanics, computer animation, and humancomputer interaction. Current motion capture methods typically use human body models in order to guide pose estimation and tracking. We model the human body as a set of tapered superquadrics connected in an articulated structure and propose an algorithm to automatically estimate the parameters of the model using video sequences obtained from multiple calibrated cameras. Our method is based on the fact that the human body is constructed of several articulated chains that can be visualised as essentially 1D segments embedded in 3D space and connected at specific joint locations. The proposed method first computes a voxel representation from the images and maps the voxels to a high dimensional space in order to extract the 1D structure. A bottomup approach is then suggested in order to build a parametric (splinebased) representation of a general articulated body in the high dimensional space followed by a topdown probabilistic approach that registers the segments to the known human body model. We then present an algorithm to estimate the parameters of our model using the segmented and registered voxels. 1
Nonlinear Spherical Shells for Approximate Principal Curves Skeletonization
"... We present Nonlinear Spherical Shells (NSS) as a noniterative modelfree method for constructing approximate principal curves skeletons in volumes of d dimensional data points. NSS leverages existing modelfree techniques for nonlinear dimension to remove nonlinear artifacts in data. With nonlinear ..."
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Cited by 4 (3 self)
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We present Nonlinear Spherical Shells (NSS) as a noniterative modelfree method for constructing approximate principal curves skeletons in volumes of d dimensional data points. NSS leverages existing modelfree techniques for nonlinear dimension to remove nonlinear artifacts in data. With nonlinearities removed and topology preserved, data embedded by such procedures are assumed to have properties amenable to simple skeletonization procedures. Given these assumptions, NSS is able extract points in the “middle ” of the volume data and hierarchically link them into principal curves, or a set of 1manifolds connected at junctions. 1.