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114
Fast pose estimation with parametersensitive hashing
 In ICCV
, 2003
"... Examplebased methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality of the input is low. For complex and highdimensional problems such as pose estimation, the number of required examples and the computational complexity rapidly become pro ..."
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Cited by 250 (8 self)
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Examplebased methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality of the input is low. For complex and highdimensional problems such as pose estimation, the number of required examples and the computational complexity rapidly become prohibitively high. We introduce a new algorithm that learns a set of hashing functions that efficiently index examples in a way relevant to a particular estimation task. Our algorithm extends localitysensitive hashing, a recently developed method to find approximate neighbors in time sublinear in the number of examples. This method depends critically on the choice of hash functions; we show how to find the set of hash functions that are optimally relevant to a particular estimation problem. Experiments demonstrate that the resulting algorithm, which we call ParameterSensitive Hashing, can rapidly and accurately estimate the articulated pose of human figures from a large database of example images. 1.
Estimating 3D Hand Pose From a Cluttered Image
, 2003
"... A method is proposed that can generate a ranked list of plausible threedimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of s ..."
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Cited by 173 (7 self)
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A method is proposed that can generate a ranked list of plausible threedimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel cluttertolerant indexing methods. First, a computationally efficient approximation of the imagetomodel chamfer distance is obtained by embedding binary edge images into a highdimensional Euclidean space. Second, a generalpurpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this cluttertolerant approach is demonstrated in quantitative experiments with hundreds of real hand images.
Fast pose estimation with parameter sensitive hashing
 In ICCV
, 2003
"... Examplebased methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality of the input is low. For complex and highdimensional problems such as pose estimation, the number of required examples and the computational complexity rapidly become pro ..."
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Cited by 114 (4 self)
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Examplebased methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality of the input is low. For complex and highdimensional problems such as pose estimation, the number of required examples and the computational complexity rapidly become prohibitively high. We introduce a new algorithm that learns a set of hashing functions that efficiently index examples relevant to a particular estimation task. Our algorithm extends a recently developed method for localitysensitive hashing, which finds approximate neighbors in time sublinear in the number of examples. This method depends critically on the choice of hash functions; we show how to find the set of hash functions that are optimally relevant to a particular estimation problem. Experiments demonstrate that the resulting algorithm, which we call ParameterSensitive Hashing, can rapidly and accurately estimate the articulated pose of human figures from a large database of example images. 1.
ModelBased Hand Tracking Using A Hierarchical Bayesian Filter
, 2004
"... This thesis focuses on the automatic recovery of threedimensional hand motion from one or more views. A 3D geometric hand model is constructed from truncated cones, cylinders and ellipsoids and is used to generate contours, which can be compared with edge contours and skin colour in images. The han ..."
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Cited by 104 (3 self)
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This thesis focuses on the automatic recovery of threedimensional hand motion from one or more views. A 3D geometric hand model is constructed from truncated cones, cylinders and ellipsoids and is used to generate contours, which can be compared with edge contours and skin colour in images. The hand tracking problem is formulated as state estimation, where the model parameters define the internal state, which is to be estimated from image observations. In thew first
ModelBased 3D Tracking of an Articulated Hand
"... This paper presents a practical technique for modelbased 3D hand tracking. An anatomically accurate hand model is built from truncated quadrics. This allows for the generation of 2D profiles of the model using elegant tools from projective geometry, and for an efficient method to handle selfocclusi ..."
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Cited by 70 (0 self)
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This paper presents a practical technique for modelbased 3D hand tracking. An anatomically accurate hand model is built from truncated quadrics. This allows for the generation of 2D profiles of the model using elegant tools from projective geometry, and for an efficient method to handle selfocclusion. The pose of the hand model is estimated with an Unscented Kalman filter (UKF), which minimizes the geometric error between the profiles and edges extracted from the images. The use of the UKF permits higher frame rates than more sophisticated estimation methods such as particle filtering, whilst providing higher accuracy than the extended Kalman filter. The system is easily scalable from single to multiple views, and from rigid to articulated models. First experiments on real data using one and two cameras demonstrate the quality of the proposed method for tracking a 7 DOF hand model.
Visionbased hand pose estimation: A review
, 2007
"... Direct use of the hand as an input device is an attractive method for providing natural human–computer interaction (HCI). Currently, the only technology that satisfies the advanced requirements of handbased input for HCI is glovebased sensing. This technology, however, has several drawbacks includ ..."
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Cited by 61 (1 self)
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Direct use of the hand as an input device is an attractive method for providing natural human–computer interaction (HCI). Currently, the only technology that satisfies the advanced requirements of handbased input for HCI is glovebased sensing. This technology, however, has several drawbacks including that it hinders the ease and naturalness with which the user can interact with the computercontrolled environment, and it requires long calibration and setup procedures. Computer vision (CV) has the potential to provide more natural, noncontact solutions. As a result, there have been considerable research efforts to use the hand as an input device for HCI. information corresponding to motion patterns or postures of the hand. The second is based on pose estimation systems and aims to capture the real 3D motion of the hand. This paper presents a literature review on the latter research direction, which is a very challenging problem in the context of HCI.
Distributed occlusion reasoning for tracking with nonparametric belief propagation
 In NIPS
, 2004
"... We describe a three–dimensional geometric hand model suitable for visual tracking applications. The kinematic constraints implied by the model’s joints have a probabilistic structure which is well described by a graphical model. Inference in this model is complicated by the hand’s many degrees of fr ..."
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Cited by 60 (0 self)
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We describe a three–dimensional geometric hand model suitable for visual tracking applications. The kinematic constraints implied by the model’s joints have a probabilistic structure which is well described by a graphical model. Inference in this model is complicated by the hand’s many degrees of freedom, as well as multimodal likelihoods caused by ambiguous image measurements. We use nonparametric belief propagation (NBP) to develop a tracking algorithm which exploits the graph’s structure to control complexity, while avoiding costly discretization. While kinematic constraints naturally have a local structure, self– occlusions created by the imaging process lead to complex interpendencies in color and edge–based likelihood functions. However, we show that local structure may be recovered by introducing binary hidden variables describing the occlusion state of each pixel. We augment the NBP algorithm to infer these occlusion variables in a distributed fashion, and then analytically marginalize over them to produce hand position estimates which properly account for occlusion events. We provide simulations showing that NBP may be used to refine inaccurate model initializations, as well as track hand motion through extended image sequences. 1
Tracking Articulated Body by Dynamic Markov Network
 PROC. IEEE INT'L CONF. ON COMPUTER VISION, NICE, FRANCE
, 2003
"... A new method for visual tracking of articulated objects is presented. Analyzing articulated motion is challenging because the dimensionality increase potentially demands tremendous increase of computation. To ease this problem, we propose an approach that analyzes subparts locally while reinforcing ..."
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Cited by 59 (9 self)
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A new method for visual tracking of articulated objects is presented. Analyzing articulated motion is challenging because the dimensionality increase potentially demands tremendous increase of computation. To ease this problem, we propose an approach that analyzes subparts locally while reinforcing the structural constraints at the mean time. The computational model of the proposed approach is based on a dynamic Markov network, a generative model which characterizes the dynamics and the image observations of each individual subpart as well as the motion constraints among different subparts. Probabilistic variational analysis of the model reveals a mean field approximation to the posterior densities of each subparts given visual evidence, and provides a computationally efficient way for such a difficult Bayesian inference problem. In addition, we design mean field Monte Carlo (MFMC) algorithms, in which a set of low dimensional particle filters interact with each other and solve the high dimensional problem collaboratively. Extensive experiments on tracking human body parts demonstrate the effectiveness, significance and computational efficiency of the proposed method.
Using Multiple Cues for Hand Tracking and Model Refinement
 In International Conference on Computer Vision and Pattern Recognition
, 2003
"... We present a model based approach to the integration of multiple cues for tracking high degree of freedom articulated motions and model refinement. We then apply it to the problem of hand tracking using a single camera sequence. Hand tracking is particularly challenging because of occlusions, shadin ..."
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Cited by 52 (8 self)
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We present a model based approach to the integration of multiple cues for tracking high degree of freedom articulated motions and model refinement. We then apply it to the problem of hand tracking using a single camera sequence. Hand tracking is particularly challenging because of occlusions, shading variations, and the high dimensionality of the motion. The novelty of our approach is in the combination of multiple sources of information which come from edges, optical flow and shading information in order to refine the model during tracking. We first use a previously formulated generalized version of the gradientbased optical flow constraint, that includes shading flow i.e., the variation of the shading of the object as it rotates with respect to the light source. Using this model we track its complex articulated motion in the presence of shading changes. We use a forward recursive dynamic model to track the motion in response to data derived 3D forces applied to the model. However, due to inaccurate initial shape the generalized optical flow constraint is violated. In this paper we use the error in the generalized optical flow equation to compute generalized forces that correct the model shape at each step. The effectiveness of our approach is demonstrated with experiments on a number of different hand motions with shading changes, rotations and occlusions of significant parts of the hand.
An AppearanceBased Framework for 3D Hand Shape Classification and Camera Viewpoint Estimation
, 2002
"... An appearancebased framework for 3D hand shape classification and simultaneous camera viewpoint estimation is presented. Given an input image of a segmented hand, the most similar matches from a large database of synthetic hand images are retrieved. The ground truth labels of those matches, contain ..."
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Cited by 50 (4 self)
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An appearancebased framework for 3D hand shape classification and simultaneous camera viewpoint estimation is presented. Given an input image of a segmented hand, the most similar matches from a large database of synthetic hand images are retrieved. The ground truth labels of those matches, containing hand shape and camera viewpoint information, are returned by the system as estimates for the input image. Database retrieval is done hierarchically, by first quickly rejecting the vast majority of all database views, and then ranking the remaining candidates in order of similarity to the input. Four different similarity measures are employed, based on edge location, edge orientation, finger location and geometric moments.