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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 321 (54 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
The Computation of Optical Flow
, 1995
"... Twodimensional image motion is the projection of the threedimensional motion of objects, relative to a visual sensor, onto its image plane. Sequences of timeordered images allow the estimation of projected twodimensional image motion as either instantaneous image velocities or discrete image dis ..."
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Cited by 274 (10 self)
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Twodimensional image motion is the projection of the threedimensional motion of objects, relative to a visual sensor, onto its image plane. Sequences of timeordered images allow the estimation of projected twodimensional image motion as either instantaneous image velocities or discrete image displacements. These are usually called the optical flow field or the image velocity field. Provided that optical flow is a reliable approximation to twodimensional image motion, it may then be used to recover the threedimensional motion of the visual sensor (to within a scale factor) and the threedimensional surface structure (shape or relative depth) through assumptions concerning the structure of the optical flow field, the threedimensional environment and the motion of the sensor. Optical flow may also be used to perform motion detection, object segmentation, timetocollision and focus of expansion calculations, motion compensated encoding and stereo disparity measurement. We investiga...
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
, 1994
"... A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot use ..."
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Cited by 270 (9 self)
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A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses twodimensional laser range scans for localization, it is difficult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans. In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The first algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacementbetween the scans. The second algorithm establishes correspondences between points in the two scans and then solves the pointtopoint leastsquares probl...
On the Unification Line Processes, Outlier Rejection, and Robust Statistics with Applications in Early Vision
, 1996
"... The modeling of spatial discontinuities for problems such as surface recovery, segmentation, image reconstruction, and optical flow has been intensely studied in computer vision. While "lineprocess" models of discontinuities have received a great deal of attention, there has been recent ..."
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Cited by 253 (9 self)
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The modeling of spatial discontinuities for problems such as surface recovery, segmentation, image reconstruction, and optical flow has been intensely studied in computer vision. While "lineprocess" models of discontinuities have received a great deal of attention, there has been recent interest in the use of robust statistical techniques to account for discontinuities. This paper unifies the two approaches. To achieve this we generalize the notion of a "line process" to that of an analog "outlier process" and show how a problem formulated in terms of outlier processes can be viewed in terms of robust statistics. We also characterize a class of robust statistical problems for which an equivalent outlierprocess formulation exists and give a straightforward method for converting a robust estimation problem into an outlierprocess formulation. We show how prior assumptions about the spatial structure of outliers can be expressed as constraints on the recovered analog outlier processes and how traditional continuation methods can be extended to the explicit outlierprocess formulation. These results indicate that the outlierprocess approach provides a general framework which subsumes the traditional lineprocess approaches as well as a wide class of robust estimation problems. Examples in surface reconstruction, image segmentation, and optical flow are presented to illustrate the use of outlier processes and to show how the relationship between outlier processes and robust statistics can be exploited. An appendix provides a catalog of common robust error norms and their equivalent outlierprocess formulations.
A framework for the robust estimation of optical flow
 in Computer Vision
, 1993
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Mixture Models for Optical Flow Computation
, 1993
"... The computahon of optical flow rehes on merg. ,ng znformat,on avadable over an zmage patch to form an estimate of D mage veloct!t at a point. Ths merging process rases a host of ssues, which include the treatment of outhers m component ve !oc*t!t measurements and the modehng of mulhple motions wath ..."
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Cited by 154 (16 self)
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The computahon of optical flow rehes on merg. ,ng znformat,on avadable over an zmage patch to form an estimate of D mage veloct!t at a point. Ths merging process rases a host of ssues, which include the treatment of outhers m component ve !oc*t!t measurements and the modehng of mulhple motions wathm a patch whzch arse from occlusion boundaries or transparency. We present a new ap proach for deahno wth these ssues. which s based Proc. CVPR'93, New York, June 1993 2 a c Figure 2: Multiple motion constraint lines for the region in Figure I (see text).
Estimating Articulated Human Motion With Covariance Scaled Sampling
 International Journal of Robotics Research
, 2003
"... We present a method for recovering 3D human body motion from monocular video sequences based on a robust image matching metric, incorporation of joint limits and nonselfintersection constraints, and a new sampleandrefine search strategy guided by rescaled costfunction covariances. Monocular 3D ..."
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Cited by 118 (10 self)
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We present a method for recovering 3D human body motion from monocular video sequences based on a robust image matching metric, incorporation of joint limits and nonselfintersection constraints, and a new sampleandrefine search strategy guided by rescaled costfunction covariances. Monocular 3D body tracking is challenging: besides the difficulty of matching an imperfect, highly flexible, selfoccluding model to cluttered image features, realistic body models have at least 30 joint parameters subject to highly nonlinear physical constraints, and at least a third of these degrees of freedom are nearly unobservable in any given monocular image. For image matching we use a carefully designed robust cost metric combining robust optical flow, edge energy, and motion boundaries. The nonlinearities and matching ambiguities make the parameterspace cost surface multimodal, illconditioned and highly nonlinear, so searching it is difficult. We discuss the limitations of CONDENSATIONlike samplers, and describe a novel hybrid search algorithm that combines inflatedcovariancescaled sampling and robust continuous optimization subject to physical constraints and model priors. Our experiments on challenging monocular sequences show that robust cost modeling, joint and selfintersection constraints, and informed sampling are all essential for reliable monocular 3D motion estimation.
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 112 (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...
Ordinal Measures for Image Correspondence
, 1998
"... We present ordinal measures of association for image correspondence in the context of stereo. Linear correspondence measures like correlation and the sum of squared difference between intensity distributions are known to be fragile. Ordinal measures which are based on relative ordering of intensity ..."
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Cited by 82 (0 self)
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We present ordinal measures of association for image correspondence in the context of stereo. Linear correspondence measures like correlation and the sum of squared difference between intensity distributions are known to be fragile. Ordinal measures which are based on relative ordering of intensity values in windows  rank permutations  have demonstrable robustness. By using distance metrics between two rank permutations, ordinal measures are defined. These measures are independent of absolute intensity scale and invariant to monotone transformations of intensity values like gamma variation between images. We have developed simple algorithms for their efficient implementation. Experiments suggest the superiority of ordinal measures over existing techniques under nonideal conditions. These measures serve as a general tool for image matching that are applicable to other vision problems such as motion estimation and texturebased image retrieval. Keywords: Image matching, Stereo, Ordi...
Modelbased 2D&3D Dominant Motion Estimation for Mosaicing and Video Representation
, 1995
"... It is fairly common in video sequences that a mostly fixed background (scene) is imaged with or without independently moving objects. The dominant background changes in the image plane mostly due to camera operations and motion (zoom, pan, tilt, track etc.). In this paper we address the problem of c ..."
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Cited by 57 (3 self)
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It is fairly common in video sequences that a mostly fixed background (scene) is imaged with or without independently moving objects. The dominant background changes in the image plane mostly due to camera operations and motion (zoom, pan, tilt, track etc.). In this paper we address the problem of computation of the dominant image transformation over time and demonstrate how this can be effectively used for efficient video representation through video mosaicing and image registration. We formulate the problem of dominant component estimation as that of modelbased robust estimation using Mestimators with direct, multiresolution methods. In addition to 2D affine and plane projective models, that have been used in the past, for describing image motion using direct methods, we also employ a true 3D model of motion and scene structure imaged with uncalibrated cameras. This model parameterizes the image motion as that due to a planar component and a parallax component. For rigid 3D scenes...