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44
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 77 (12 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 35 (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.
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 26 (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
3D skeletonbased body pose recovery
 In 3D Data Processing, Visualization and Transmission
, 2006
"... HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte p ..."
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Cited by 25 (1 self)
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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Human Motion Estimation from a Reduced Marker Set
 in Proceedings of ACM Symposium on Interactive 3D Graphics and Games 2006
, 2006
"... Abstract Motion capture data from human subjects exhibits considerable redundancy. In this paper, we propose novel methods for exploiting this redundancy. In particular, we set out to find a subset of motioncapture markers that are able to provide fast and highquality predictions of the remaining ..."
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Cited by 19 (3 self)
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Abstract Motion capture data from human subjects exhibits considerable redundancy. In this paper, we propose novel methods for exploiting this redundancy. In particular, we set out to find a subset of motioncapture markers that are able to provide fast and highquality predictions of the remaining markers. We then develop a model that uses this reduced marker set to predict the others. We demonstrate that this subset of original markers is sufficient to capture subtle variations in human motion. We take a datadriven modeling approach to learn piecewise local linear models from a markerbased training set. We first divide motion sequences into segments of low dimensionality. We then retrieve a feature vector from each of the motion segments and use these feature vectors as modeling primitives to cluster the segments into a hierarchy of local linear models via a divisive clustering method. The selection of an appropriate linear model for reconstruction of a fullbody pose is determined automatically via a classifier driven by a reduced marker set. After offline training, our method can quickly reconstruct fullbody human motion using a reduced marker set without storing and searching the large database. We also demonstrate our method's ability to generalize over a variety of motions from multiple subjects.
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 14 (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.
Markerless motion capture using multiple cameras
 In Computer Vision for Interactive and Intelligent Environment
, 2005
"... Motion capture has important applications in different areas such as biomechanics, computer animation, and humancomputer interaction. Current motion capture methods use passive markers that are attached to different body parts of the subject and are therefore intrusive in nature. In applications su ..."
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Cited by 8 (0 self)
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Motion capture has important applications in different areas such as biomechanics, computer animation, and humancomputer interaction. Current motion capture methods use passive markers that are attached to different body parts of the subject and are therefore intrusive in nature. In applications such as pathological human movement analysis, these markers may introduce an unknown artifact in the motion, and are, in general, cumbersome. We present computer vision based methods for performing markerless human motion capture. We model the human body as a set of superquadrics connected in an articulated structure and propose algorithms to estimate the parameters of the model from video sequences. We compute a volume data (voxel) representation from the images and combine bottomup approach with top down approach guided by our knowledge of the model. We propose a tracking algorithm that uses this model to track human pose. The tracker uses an iterative framework akin to an Iterated Extended Kalman Filter to estimate articulated human motion using multiple cues that combine both spatial and temporal information in a novel manner. We provide preliminary results using data collected from 816 cameras. The emphasis of our work is on models and algorithms that are able to scale with respect to the requirement for accuracy. Our ultimate objective is to build an endtoend system that can integrate the above mentioned components into a completely automated markerless motion capture system. 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 7 (3 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.
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 7 (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.
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 6 (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.