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254
Robust multiresolution estimation of parametric motion models
 Jal of Vis. Comm. and Image Representation
, 1995
"... This paper describes a method to estimate parametric motion models. Motivations for the use of such models are on one hand their efficiency, which has been demonstrated in numerous contexts such as estimation, segmentation, tracking and interpretation of motion, and on the other hand, their low comp ..."
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Cited by 329 (55 self)
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This paper describes a method to estimate parametric motion models. Motivations for the use of such models are on one hand their efficiency, which has been demonstrated in numerous contexts such as estimation, segmentation, tracking and interpretation of motion, and on the other hand, their low computational cost compared to optical flow estimation. However, it is important to have the best accuracy for the estimated parameters, and to take into account the problem of multiple motion. We have therefore developed two robust estimators in a multiresolution framework. Numerical results support this approach, as validated by the use of these algorithms on complex sequences. 1
A Framework for Robust Subspace Learning
 International Journal of Computer Vision
, 2003
"... Many computer vision, signal processing and statistical problems can be posed as problems of learning low dimensional linear or multilinear models. These models have been widely used for the representation of shape, appearance, motion, etc, in computer vision applications. ..."
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Cited by 177 (10 self)
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Many computer vision, signal processing and statistical problems can be posed as problems of learning low dimensional linear or multilinear models. These models have been widely used for the representation of shape, appearance, motion, etc, in computer vision applications.
Robust parameter estimation in computer vision
 SIAM Reviews
, 1999
"... Abstract. Estimation techniques in computer vision applications must estimate accurate model parameters despite smallscale noise in the data, occasional largescale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techni ..."
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Cited by 164 (10 self)
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Abstract. Estimation techniques in computer vision applications must estimate accurate model parameters despite smallscale noise in the data, occasional largescale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techniques, some borrowed from the statistics literature and others described in the computer vision literature, have been used in solving these parameter estimation problems. Ideally, these techniques should effectively ignore the outliers and measurements from other populations, treating them as outliers, when estimating the parameters of a single population. Two frequently used techniques are leastmedian of
Enhancing Sparsity by Reweighted ℓ1 Minimization
, 2007
"... It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained ℓ1 minimization. In this paper, we study a novel method for sparse signal recovery that in many si ..."
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Cited by 145 (4 self)
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It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained ℓ1 minimization. In this paper, we study a novel method for sparse signal recovery that in many situations outperforms ℓ1 minimization in the sense that substantially fewer measurements are needed for exact recovery. The algorithm consists of solving a sequence of weighted ℓ1minimization problems where the weights used for the next iteration are computed from the value of the current solution. We present a series of experiments demonstrating the remarkable performance and broad applicability of this algorithm in the areas of sparse signal recovery, statistical estimation, error correction and image processing. Interestingly, superior gains are also achieved when our method is applied to recover signals with assumed nearsparsity in overcomplete representations—not by reweighting the ℓ1 norm of the coefficient sequence as is common, but by reweighting the ℓ1 norm of the transformed object. An immediate consequence is the possibility of highly efficient data acquisition protocols by improving on a technique known as compressed sensing.
Dense estimation of fluid flows
 IEEE Trans. Pattern Anal. Machine Intell
, 2002
"... AbstractÐIn this paper, we address the problem of estimating and analyzing the motion of fluids in image sequences. Due to the great deal of spatial and temporal distortions that intensity patterns exhibit in images of fluids, the standard techniques from Computer Vision, originally designed for qua ..."
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Cited by 127 (41 self)
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AbstractÐIn this paper, we address the problem of estimating and analyzing the motion of fluids in image sequences. Due to the great deal of spatial and temporal distortions that intensity patterns exhibit in images of fluids, the standard techniques from Computer Vision, originally designed for quasirigid motions with stable salient features, are not well adapted in this context. We thus investigate a dedicated minimizationbased motion estimator. The cost function to be minimized includes a novel data term relyingon an integrated version of the continuity equation of fluid mechanics, which is compatible with large displacements. This term is associated with an original secondorder divcurl regularization which prevents the washing out of the salient vorticity and divergence structures. The performance of the resulting fluid flow estimator is demonstrated on meteorological satellite images. In addition, we show how the sequences of dense motion fields we estimate can be reliably used to reconstruct trajectories and to extract the regions of high vorticity and divergence. Index TermsÐFluid motion, continuity equation, divcurl regularization, nonconvex minimization, trajectories, vorticity, and divergence concentration. 1
Realtime markerless tracking for augmented reality: the virtual visual servoing framework
 IEEE TRANS. ON VISUALIZATION AND COMPUTER GRAPHICS
, 2006
"... Tracking is a very important research subject in a realtime augmented reality context. The main requirements for trackers are high accuracy and little latency at a reasonable cost. In order to address these issues, a realtime, robust, and efficient 3D modelbased tracking algorithm is proposed for ..."
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Cited by 114 (29 self)
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Tracking is a very important research subject in a realtime augmented reality context. The main requirements for trackers are high accuracy and little latency at a reasonable cost. In order to address these issues, a realtime, robust, and efficient 3D modelbased tracking algorithm is proposed for a “video see through ” monocular vision system. The tracking of objects in the scene amounts to calculating the pose between the camera and the objects. Virtual objects can then be projected into the scene using the pose. Here, nonlinear pose estimation is formulated by means of a virtual visual servoing approach. In this context, the derivation of pointtocurves interaction matrices are given for different 3D geometrical primitives including straight lines, circles, cylinders, and spheres. A local moving edges tracker is used in order to provide realtime tracking of points normal to the object contours. Robustness is obtained by integrating an Mestimator into the visual control law via an iteratively reweighted least squares implementation. This approach is then extended to address the 3D modelfree augmented reality problem. The method presented in this paper has been validated on several complex image sequences including outdoor environments. Results show the method to be robust to occlusion, changes in illumination, and mistracking.
Dense Estimation and ObjectBased Segmentation of the Optical Flow with Robust Techniques
, 1998
"... In this paper we address the issue of recovering and segmenting the apparent velocity field in sequences of images. As for motion estimation, we minimize an objective function involving two robust terms. The first one cautiously captures the optical flow constraint, while the second (a priori) term ..."
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Cited by 113 (20 self)
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In this paper we address the issue of recovering and segmenting the apparent velocity field in sequences of images. As for motion estimation, we minimize an objective function involving two robust terms. The first one cautiously captures the optical flow constraint, while the second (a priori) term incorporates a discontinuitypreserving smoothness constraint. To cope with the nonconvex minimization problem thus defined, we design an efficient deterministic multigrid procedure. It converges fast toward estimates of good quality, while revealing the large discontinuity structures of flow fields. We then propose an extension of the model by attaching to it a flexible objectbased segmentation device based on deformable closed curves (different families of curve equipped with different kinds of prior can be easily supported). Experimental results on synthetic and natural sequences are presented, including an analysis of sensitivity to parameter tuning. INdex Terms Closed segmenting cu...
Robust clustering methods: a unified view
 IEEE Transactions on Fuzzy Systems
, 1997
"... Abstract—Clustering methods need to be robust if they are to be useful in practice. In this paper, we analyze several popular robust clustering methods and show that they have much in common. We also establish a connection between fuzzy set theory and robust statistics and point out the similarities ..."
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Cited by 112 (8 self)
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Abstract—Clustering methods need to be robust if they are to be useful in practice. In this paper, we analyze several popular robust clustering methods and show that they have much in common. We also establish a connection between fuzzy set theory and robust statistics and point out the similarities between robust clustering methods and statistical methods such as the weighted leastsquares (LS) technique, the M estimator, the minimum volume ellipsoid (MVE) algorithm, cooperative robust estimation (CRE), minimization of probability of randomness (MINPRAN), and the epsilon contamination model. By gleaning the common principles upon which the methods proposed in the literature are based, we arrive at a unified view of robust clustering methods. We define several general concepts that are useful in robust clustering, state the robust clustering problem in terms of the defined concepts, and propose generic algorithms and guidelines for clustering noisy data. We also discuss why the generalized Hough transform is a suboptimal solution to the robust clustering problem. Index Terms — Clustering validity, fuzzy clustering, robust methods.
The dualbootstrap iterative closest point algorithm with application to retinal image registration
 IEEE Trans. Med. Img
, 2003
"... Abstract—Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called DualBootstrap Iterative Closest Point (DualBootstrap ICP). The approach is to start from one or more initial, loworder estimates that are only accurate in small ..."
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Cited by 88 (19 self)
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Abstract—Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called DualBootstrap Iterative Closest Point (DualBootstrap ICP). The approach is to start from one or more initial, loworder estimates that are only accurate in small image regions, called bootstrap regions. In each bootstrap region, the algorithm iteratively: 1) refines the transformation estimate using constraints only from within the bootstrap region; 2) expands the bootstrap region; and 3) tests to see if a higher order transformation model can be used, stopping when the region expands to cover the overlap between images. Steps 1): and 3), the bootstrap steps, are governed by the covariance matrix of the estimated transformation. Estimation refinement [Step 2)] uses a novel robust version of the ICP algorithm. In registering retinal image pairs, DualBootstrap ICP is initialized by automatically matching individual vascular landmarks, and it aligns images based on detected blood vessel centerlines. The resulting quadratic transformations are accurate to less than a pixel. On tests involving approximately 6000 image pairs, it successfully registered 99.5 % of the pairs containing at least one common landmark, and 100 % of the pairs containing at least one common landmark and at least 35 % image overlap. Index Terms—Iterative closest point, medical imaging, registration, retinal imaging, robust estimation.
An EMlike algorithm for colorhistogrambased object tracking, in
 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
"... The iterative procedure called ’meanshift ’ is a simple robust method for finding the position of a local mode (local maximum) of a kernelbased estimate of a density function. A new robust algorithm is given here that presents a natural extension of the ’meanshift ’ procedure. The new algorithm s ..."
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Cited by 77 (6 self)
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The iterative procedure called ’meanshift ’ is a simple robust method for finding the position of a local mode (local maximum) of a kernelbased estimate of a density function. A new robust algorithm is given here that presents a natural extension of the ’meanshift ’ procedure. The new algorithm simultaneously estimates the position of the local mode and the covariance matrix that describes the approximate shape of the local mode. We apply the new method to develop a new 5degrees of freedom (DOF) color histogram based nonrigid object tracking algorithm. 1.