The Experts below are selected from a list of 11445 Experts worldwide ranked by ideXlab platform
Philip H S Torr - One of the best experts on this subject based on the ideXlab platform.
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simultaneous segmentation and pose estimation of humans using dynamic graph cuts
International Journal of Computer Vision, 2008Co-Authors: Pushmeet Kohli, Jonathan Rihan, Matthieu Bray, Philip H S TorrAbstract:This paper presents a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other state of the art methods which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Our method works by optimizing a cost function based on a Conditional Random Field (CRF). This has the advantage that all information in the image (edges, background and foreground appearances), as well as the prior information on the shape and pose of the subject can be combined and used in a Bayesian framework. Optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any rigid, deformable or Articulated Object.
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posecut simultaneous segmentation and 3d pose estimation of humans using dynamic graph cuts
Lecture Notes in Computer Science, 2006Co-Authors: Matthieu Bray, Pushmeet Kohli, Philip H S TorrAbstract:We present a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other related state of the art techniques which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Normally, when optimizing for pose, it is traditional to use some fixed set of features, e.g. edges or chamfer maps. In contrast, our novel approach consists of optimizing a cost function based on a Markov Random Field (MRF). This has the advantage that we can use all the information in the image: edges, background and foreground appearances, as well as the prior information on the shape and pose of the subject and combine them in a Bayesian framework. Previously, optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Note that although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any specified rigid, deformable or Articulated Object.
Pushmeet Kohli - One of the best experts on this subject based on the ideXlab platform.
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simultaneous segmentation and pose estimation of humans using dynamic graph cuts
International Journal of Computer Vision, 2008Co-Authors: Pushmeet Kohli, Jonathan Rihan, Matthieu Bray, Philip H S TorrAbstract:This paper presents a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other state of the art methods which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Our method works by optimizing a cost function based on a Conditional Random Field (CRF). This has the advantage that all information in the image (edges, background and foreground appearances), as well as the prior information on the shape and pose of the subject can be combined and used in a Bayesian framework. Optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any rigid, deformable or Articulated Object.
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posecut simultaneous segmentation and 3d pose estimation of humans using dynamic graph cuts
Lecture Notes in Computer Science, 2006Co-Authors: Matthieu Bray, Pushmeet Kohli, Philip H S TorrAbstract:We present a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other related state of the art techniques which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Normally, when optimizing for pose, it is traditional to use some fixed set of features, e.g. edges or chamfer maps. In contrast, our novel approach consists of optimizing a cost function based on a Markov Random Field (MRF). This has the advantage that we can use all the information in the image: edges, background and foreground appearances, as well as the prior information on the shape and pose of the subject and combine them in a Bayesian framework. Previously, optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Note that although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any specified rigid, deformable or Articulated Object.
Matthieu Bray - One of the best experts on this subject based on the ideXlab platform.
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simultaneous segmentation and pose estimation of humans using dynamic graph cuts
International Journal of Computer Vision, 2008Co-Authors: Pushmeet Kohli, Jonathan Rihan, Matthieu Bray, Philip H S TorrAbstract:This paper presents a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other state of the art methods which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Our method works by optimizing a cost function based on a Conditional Random Field (CRF). This has the advantage that all information in the image (edges, background and foreground appearances), as well as the prior information on the shape and pose of the subject can be combined and used in a Bayesian framework. Optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any rigid, deformable or Articulated Object.
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posecut simultaneous segmentation and 3d pose estimation of humans using dynamic graph cuts
Lecture Notes in Computer Science, 2006Co-Authors: Matthieu Bray, Pushmeet Kohli, Philip H S TorrAbstract:We present a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other related state of the art techniques which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Normally, when optimizing for pose, it is traditional to use some fixed set of features, e.g. edges or chamfer maps. In contrast, our novel approach consists of optimizing a cost function based on a Markov Random Field (MRF). This has the advantage that we can use all the information in the image: edges, background and foreground appearances, as well as the prior information on the shape and pose of the subject and combine them in a Bayesian framework. Previously, optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Note that although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any specified rigid, deformable or Articulated Object.
Marc Pollefeys - One of the best experts on this subject based on the ideXlab platform.
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A Factorization-Based Approach for Articulated Nonrigid Shape, Motion and Kinematic Chain Recovery From Video
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008Co-Authors: Jingyu Yan, Marc PollefeysAbstract:Recovering Articulated shape and motion, especially human body motion, from video is a challenging problem with a wide range of applications in medical study, sport analysis, animation, and so forth. Previous work on Articulated motion recovery generally requires prior knowledge of the kinematic chain and usually does not concern the recovery of the Articulated shape. The nonrigidity of some Articulated part, for example, human body motion with nonrigid facial motion, is completely ignored. We propose a factorization-based approach to recover the shape, motion, and kinematic chain of an Articulated Object with nonrigid parts altogether directly from video sequences under a unified framework. The proposed approach is based on our modeling of the Articulated nonrigid motion as a set of intersecting motion subspaces. A motion subspace is the linear subspace of the trajectories of an Object. It can model a rigid or nonrigid motion. The intersection of two motion subspaces of linked parts models the motion of an Articulated joint or axis. Our approach consists of algorithms for motion segmentation, kinematic chain building, and shape recovery. It handles outliers and can be automated. We test our approach through synthetic and real experiments and demonstrate how to recover an Articulated structure with nonrigid parts via a single-view camera without prior knowledge of its kinematic chain.
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automatic kinematic chain building from feature trajectories of Articulated Objects
Computer Vision and Pattern Recognition, 2006Co-Authors: Jingyu Yan, Marc PollefeysAbstract:We investigate the problem of learning the structure of an Articulated Object, i.e. its kinematic chain, from feature trajectories under affine projections. We demonstrate this possibility by proposing an algorithm which first segments the trajectories by local sampling and spectral clustering, then builds the kinematic chain as a minimum spanning tree of a graph constructed from the segmented motion subspaces. We test our method in challenging data sets and demonstrate the ability to automatically build the kinematic chain of an Articulated Object from feature trajectories. The algorithm also works when there are multiple Articulated Objects in the scene. Furthermore, we take into account non-rigid Articulated parts that exist in human motions. We believe this advance will have impact on Articulated Object tracking and dynamical structure from motion.
Liu C. Karen - One of the best experts on this subject based on the ideXlab platform.
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Estimating Mass Distribution of Articulated Objects through Non-prehensile Manipulation
2020Co-Authors: Kumar K. Niranjan, Essa Irfan, Liu C. KarenAbstract:We explore the problem of estimating the mass distribution of an Articulated Object by an interactive robotic agent. Our method predicts the mass distribution of an Object by using limited sensing and actuating capabilities of a robotic agent during an interaction with the Object. Inspired by the role of exploratory play in human infants, we take the combined approach of supervised and reinforcement learning to train an agent such that it learns to strategically interact with the Object for estimating its mass distribution. Our method consists of two neural networks: (i) the policy network which decides how to interact with the Object, and (ii) the predictor network that estimates the mass distribution given a history of observations and interactions. Using our method, we train a robotic arm to estimate the mass distribution of an Object with moving parts (e.g. an Articulated rigid body system) by pushing it on a surface with unknown friction properties. We show the robustness of our method across different physics simulators and robotic platforms. We further test our method on a real robot platform with 3D printed Articulated chains with varying mass distributions. We present results that demonstrate that our method significantly outperforms the baseline agent that uses random pushes to interact with the Object
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Estimating Mass Distribution of Articulated Objects using Non-prehensile Manipulation
2020Co-Authors: Kumar K. Niranjan, Essa Irfan, Ha Sehoon, Liu C. KarenAbstract:We explore the problem of estimating the mass distribution of an Articulated Object by an interactive robotic agent. Our method predicts the mass distribution of an Object by using the limited sensing and actuating capabilities of a robotic agent that is interacting with the Object. We are inspired by the role of exploratory play in human infants. We take the combined approach of supervised and reinforcement learning to train an agent that learns to strategically interact with the Object to estimate the Object's mass distribution. Our method consists of two neural networks: (i) the policy network which decides how to interact with the Object, and (ii) the predictor network that estimates the mass distribution given a history of observations and interactions. Using our method, we train a robotic arm to estimate the mass distribution of an Object with moving parts (e.g. an Articulated rigid body system) by pushing it on a surface with unknown friction properties. We also demonstrate how our training from simulations can be transferred to real hardware using a small amount of real-world data for fine-tuning. We use a UR10 robot to interact with 3D printed Articulated chains with varying mass distributions and show that our method significantly outperforms the baseline system that uses random pushes to interact with the Object
