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135
Probabilistic Deformable Surface Tracking From Multiple Videos
"... Abstract. In this paper, we address the problem of tracking the temporal evolution of arbitrary shapes observed in multicamera setups. This is motivated by the ever growing number of applications that require consistent shape information along temporal sequences. The approach we propose considers a ..."
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Cited by 41 (13 self)
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Abstract. In this paper, we address the problem of tracking the temporal evolution of arbitrary shapes observed in multicamera setups. This is motivated by the ever growing number of applications that require consistent shape information along temporal sequences. The approach we propose considers a temporal sequence of independently reconstructed surfaces and iteratively deforms a reference mesh to fit these observations. To effectively cope with outlying and missing geometry, we introduce a novel probabilistic mesh deformation framework. Using generic local rigidity priors and accounting for the uncertainty in the data acquisition process, this framework effectively handles missing data, relatively large reconstruction artefacts and multiple objects. Extensive experiments demonstrate the effectiveness and robustness of the method on various 4D datasets. 1
Deformable medical image registration: A survey
 IEEE TRANSACTIONS ON MEDICAL IMAGING
, 2013
"... Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multimodality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudin ..."
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Cited by 34 (1 self)
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Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multimodality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.
Registration of 3D Point Clouds and Meshes: A Survey From Rigid to NonRigid
"... Abstract—3D surface registration transforms multiple 3D datasets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or nonrigid registration, but seldom discuss them as a whole. Our study serves two purp ..."
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Cited by 32 (4 self)
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Abstract—3D surface registration transforms multiple 3D datasets into the same coordinate system so as to align overlapping components of these sets. Recent surveys have covered different aspects of either rigid or nonrigid registration, but seldom discuss them as a whole. Our study serves two purposes: (i) to give a comprehensive survey of both types of registration, focusing on 3D point clouds and meshes, and (ii) to provide a better understanding of registration from the perspective of data fitting. Registration is closely related to data fitting in that it comprises three core interwoven components: model selection, correspondences & constraints and optimization. Study of these components (i) provides a basis for comparison of the novelties of different techniques, (ii) reveals the similarity of rigid and nonrigid registration in terms of problem representations, and (iii) shows how overfitting arises in nonrigid registration and the reasons for increasing interest in intrinsic techniques. We further summarise some practical issues of registration which include initializations and evaluations, and discuss some of our own observations, insights and foreseeable research trends. Index Terms—Deformation modeling, digital geometry processing, surface registration, point clouds, meshes, 3D scanning 1
Accurate 3D Pose Estimation From a Single Depth Image
"... This paper presents a novel system to estimate body pose configuration from a single depth map. It combines both pose detection and pose refinement. The input depth map is matched with a set of precaptured motion exemplars to generate a body configuration estimation, as well as semantic labeling of ..."
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Cited by 23 (2 self)
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This paper presents a novel system to estimate body pose configuration from a single depth map. It combines both pose detection and pose refinement. The input depth map is matched with a set of precaptured motion exemplars to generate a body configuration estimation, as well as semantic labeling of the input point cloud. The initial estimation is then refined by directly fitting the body configuration with the observation (e.g., the input depth). In addition to the new system architecture, our other contributions include modifying a point cloud smoothing technique to deal with very noisy input depth maps, a point cloud alignment and pose search algorithm that is viewindependent and efficient. Experiments on a public dataset show that our approach achieves significantly higher accuracy than previous stateofart methods. 1.
Eigen faces for recognition
 Journal of Cognitive Neuroscience
, 1991
"... Challenge problem The Good, the Bad, and the Ugly Face Challenge Problem was created to encourage the development of algorithms that are robust to recognition across changes that occur in still frontal faces. The Good, the Bad, and the Ugly consists of three partitions. The Good partition contains ..."
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Cited by 22 (0 self)
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Challenge problem The Good, the Bad, and the Ugly Face Challenge Problem was created to encourage the development of algorithms that are robust to recognition across changes that occur in still frontal faces. The Good, the Bad, and the Ugly consists of three partitions. The Good partition contains pairs of images that are considered easy to recognize. The base verification rate (VR) is 0.98 at a false accept rate (FAR) of 0.001. The Bad partition contains pairs of images of average difficulty to recognize. For the Bad partition, the VR is 0.80 at a FAR of 0.001. The Ugly partition contains pairs of images considered difficult to recognize, with a VR of 0.15 at a FAR of 0.001. The base performance is from fusing the output of three of the top performers in the FRVT 2006. The design of the Good, the Bad, and the Ugly controls for posevariation, subject aging, and subject “recognizability. ” Subject recognizability is controlled by having the same number of images of each subject in every partition. This implies that the differences in performance among the partitions are a result of how a face is presented in each image. Published by Elsevier B.V. 1.
Robust Point Matching via Vector Field Consensus
, 2013
"... Abstract — In this paper, we propose an efficient algorithm, called vector field consensus, for establishing robust point correspondences between two sets of points. Our algorithm starts by creating a set of putative correspondences which can contain a very large number of false correspondences, or ..."
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Cited by 17 (5 self)
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Abstract — In this paper, we propose an efficient algorithm, called vector field consensus, for establishing robust point correspondences between two sets of points. Our algorithm starts by creating a set of putative correspondences which can contain a very large number of false correspondences, or outliers, in addition to a limited number of true correspondences (inliers). Next, we solve for correspondence by interpolating a vector field between the two point sets, which involves estimating a consensus of inlier points whose matching follows a nonparametric geometrical constraint. We formulate this a maximum a posteriori (MAP) estimation of a Bayesian model with hidden/latent variables indicating whether matches in the putative set are outliers or inliers. We impose nonparametric geometrical constraints on the correspondence, as a prior distribution, using Tikhonov regularizers in a reproducing kernel Hilbert space. MAP estimation is performed by the EM algorithm which by also estimating the variance of the prior model (initialized to a large value) is able to obtain good estimates very quickly (e.g., avoiding many of the local minima inherent in this formulation). We illustrate this method on data sets in 2D and 3D and demonstrate that it is robust to a very large number of outliers (even up to 90%). We also show that in the special case where there is an underlying parametric geometrical model (e.g., the epipolar line constraint) that we obtain better results than standard alternatives like RANSAC if a large number of outliers are present. This suggests a twostage strategy, where we use our nonparametric model to reduce the size of the putative set and then apply a parametric variant of our approach to estimate the geometric parameters. Our algorithm is computationally efficient and we provide code for others to use it. In addition, our approach is general and can be applied to other problems, such as learning with a badly corrupted training data set. Index Terms — Point correspondence, outlier removal, matching, regularization.
Smoothly varying affine stitching
 In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR
, 2011
"... Traditional image stitching using parametric transforms such as homography, only produces perceptually correct composites for planar scenes or parallax free camera motion between source frames. This limits mosaicing to source images taken from the same physical location. In this paper, we introduce ..."
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Cited by 17 (2 self)
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Traditional image stitching using parametric transforms such as homography, only produces perceptually correct composites for planar scenes or parallax free camera motion between source frames. This limits mosaicing to source images taken from the same physical location. In this paper, we introduce a smoothly varying affine stitching field which is flexible enough to handle parallax while retaining the good extrapolation and occlusion handling properties of parametric transforms. Our algorithm which jointly estimates both the stitching field and correspondence, permits the stitching of general motion source images, provided the scenes do not contain abrupt protrusions. 1.
A Robust method for vector field learning with application to mismatch removing
 in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
"... We propose a method for vector field learning with outliers, called vector field consensus (VFC). It could distinguish inliers from outliers and learn a vector field fitting for the inliers simultaneously. A prior is taken to force the smoothness of the field, which is based on the Tiknonov regula ..."
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Cited by 16 (7 self)
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We propose a method for vector field learning with outliers, called vector field consensus (VFC). It could distinguish inliers from outliers and learn a vector field fitting for the inliers simultaneously. A prior is taken to force the smoothness of the field, which is based on the Tiknonov regularization in vectorvalued reproducing kernel Hilbert space. Under a Bayesian framework, we associate each sample with a latent variable which indicates whether it is an inlier, and then formulate the problem as maximum a posteriori problem and use Expectation Maximization algorithm to solve it. The proposed method possesses two characteristics: 1) robust to outliers, and being able to tolerate 90 % outliers and even more, 2) computationally efficient. As an application, we apply VFC to solve the problem of mismatch removing. The results demonstrate that our method outperforms many stateoftheart methods, and it is very robust. 1.
Local multiresolution representation for 6D motion estimation and mapping with a continuously rotating 3D laser scanner
 In Proc. of IEEE Int. Conf. on Robotics and Automation (ICRA
, 2014
"... Abstract — Micro aerial vehicles (MAV) pose a challenge in designing sensory systems and algorithms due to their size and weight constraints and limited computing power. We present an efficient 3D multiresolution map that we use to aggregate measurements from a lightweight continuously rotating las ..."
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Cited by 13 (11 self)
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Abstract — Micro aerial vehicles (MAV) pose a challenge in designing sensory systems and algorithms due to their size and weight constraints and limited computing power. We present an efficient 3D multiresolution map that we use to aggregate measurements from a lightweight continuously rotating laser scanner. We estimate the robot’s motion by means of visual odometry and scan registration, aligning consecutive 3D scans with an incrementally built map. By using local multiresolution, we gain computational efficiency by having a high resolution in the near vicinity of the robot and a lower resolution with increasing distance from the robot, which correlates with the sensor’s characteristics in relative distance accuracy and measurement density. Compared to uniform grids, local multiresolution leads to the use of fewer grid cells without loosing information and consequently results in lower computational costs. We efficiently and accurately register new 3D scans with the map in order to estimate the motion of the MAV and update the map inflight. In experiments, we demonstrate superior accuracy and efficiency of our registration approach compared to stateoftheart methods such as GICP. Our approach builds an accurate 3D obstacle map and estimates the vehicle’s trajectory in realtime. I.
Asprojectiveaspossible image stitching with moving DLT
 In Proceedings of IEEE CVPR 2013
, 2013
"... We investigate projective estimation under model inadequacies, i.e., when the underpinning assumptions of the projective model are not fully satisfied by the data. We focus on the task of image stitching which is customarily solved by estimating a projective warp — a model that is justified when t ..."
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Cited by 12 (1 self)
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We investigate projective estimation under model inadequacies, i.e., when the underpinning assumptions of the projective model are not fully satisfied by the data. We focus on the task of image stitching which is customarily solved by estimating a projective warp — a model that is justified when the scene is planar or when the views differ purely by rotation. Such conditions are easily violated in practice, and this yields stitching results with ghosting artefacts that necessitate the usage of deghosting algorithms. To this end we propose asprojectiveaspossible warps, i.e., warps that aim to be globally projective, yet allow local nonprojective deviations to account for violations to the assumed imaging conditions. Based on a novel estimation technique called Moving Direct Linear Transformation (Moving DLT), our method seamlessly bridges image regions that are inconsistent with the projective model. The result is highly accurate image stitching, with significantly reduced ghosting effects, thus lowering the dependency on post hoc deghosting. 1.