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167
A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry
, 1994
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Determining the Epipolar Geometry and its Uncertainty: A Review
 International Journal of Computer Vision
, 1998
"... Two images of a single scene/object are related by the epipolar geometry, which can be described by a 3×3 singular matrix called the essential matrix if images' internal parameters are known, or the fundamental matrix otherwise. It captures all geometric information contained in two i ..."
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Cited by 400 (9 self)
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Two images of a single scene/object are related by the epipolar geometry, which can be described by a 3&times;3 singular matrix called the essential matrix if images' internal parameters are known, or the fundamental matrix otherwise. It captures all geometric information contained in two images, and its determination is very important in many applications such as scene modeling and vehicle navigation. This paper gives an introduction to the epipolar geometry, and provides a complete review of the current techniques for estimating the fundamental matrix and its uncertainty. A wellfounded measure is proposed to compare these techniques. Projective reconstruction is also reviewed. The software which we have developed for this review is available on the Internet.
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 327 (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
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 292 (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...
Efficient multiscale regularization with applications to the computation of optical flow
 IEEE Trans. Image Process
, 1994
"... AbsfruetA new approach to regularization methods for image processing is introduced and developed using as a vehicle the problem of computing dense optical flow fields in an image sequence. Standard formulations of this problem require the computationally intensive solution of an elliptic partial d ..."
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Cited by 106 (36 self)
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AbsfruetA new approach to regularization methods for image processing is introduced and developed using as a vehicle the problem of computing dense optical flow fields in an image sequence. Standard formulations of this problem require the computationally intensive solution of an elliptic partial differential equation that arises from the often used “smoothness constraint” ’yl”. regularization. The interpretation of the smoothness constraint is utilized as a “fractal prior ” to motivate regularization based on a recently introduced class of multiscale stochastic models. The solution of the new problem formulation is computed with an efficient multiscale algorithm. Experiments on several image sequences demonstrate the substantial computational savings that can be achieved due to the fact that the algorithm is noniterative and in fact has a per pixel computational complexity that is independent of image size. The new approach also has a number of other important advantages. Specifically, multiresolution flow field estimates are available, allowing great flexibility in dealing with the tradeoff between resolution and accuracy. Multiscale error covariance information is also available, which is of considerable use in assessing the accuracy of the estimates. In particular, these error statistics can be used as the basis for a rational procedure for determining the spatiallyvarying optimal reconstruction resolution. Furthermore, if there are compelling reasons to insist upon a standard smoothness constraint, our algorithm provides an excellent initialization for the iterative algorithms associated with the smoothness constraint problem formulation. Finally, the usefulness of our approach should extend to a wide variety of illposed inverse problems in which variational techniques seeking a “smooth ” solution are generally Used. I.
Distributed Representation and Analysis of Visual Motion
, 1993
"... This thesis describes some new approaches to the representation and analysis of visual motion, as perceived by a biological or machine visual system. We begin by discussing the computation of image motion fields, the projection of motion in the threedimensional world onto the twodimensional image ..."
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Cited by 68 (3 self)
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This thesis describes some new approaches to the representation and analysis of visual motion, as perceived by a biological or machine visual system. We begin by discussing the computation of image motion fields, the projection of motion in the threedimensional world onto the twodimensional image plane. This computation is notoriously difficult, and there are a wide variety of approaches that have been developed for use in image processing, machine vision, and biological modeling. We show that a large number of the basic techniques are quite similar in nature, differing primarily in conceptual motivation, and that they each fail to handle a set of situations that occur commonly in natural scenery. The central theme of the thesis is that the failure of these algorithms is due primarily to the use of vector fields as a representation for visual motion. We argue that the translational vector field representation is inherently impoverished and errorprone. Furthermore, there is evidence that a ...
Visual Control Of Robot Manipulators  A Review
 Visual Servoing
, 1994
"... This paper attempts to present a comprehensive summary of research results in the use of visual information to control robot manipulators and related mechanisms. An extensive bibliography is provided which also includes important papers from the elemental disciplines upon which visual servoing is ba ..."
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Cited by 67 (1 self)
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This paper attempts to present a comprehensive summary of research results in the use of visual information to control robot manipulators and related mechanisms. An extensive bibliography is provided which also includes important papers from the elemental disciplines upon which visual servoing is based. The research results are discussed in terms of historical context, commonality of function, algorithmic approach and method of implementation. 1 Introduction This paper presents the history, and reviews current research into the use of visual information for the control of robot manipulators and mechanisms. Visual control of manipulators promises substantial advantages when working with targets whose position is unknown, or with manipulators which may be flexible or inaccurate. The reported use of visual information to guide robots, or more generally mechanisms, is quite extensive and encompasses manufacturing applications, teleoperation, missile tracking cameras, fruit picking as well...
Extraction of 2d motion trajectories and its application to hand gesture recognition
 PAMI
, 2002
"... AbstractÐWe present an algorithm for extracting and classifying twodimensional motion in an image sequence based on motion trajectories. First, a multiscale segmentation is performed to generate homogeneous regions in each frame. Regions between consecutive frames are then matched to obtain twovie ..."
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Cited by 64 (1 self)
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AbstractÐWe present an algorithm for extracting and classifying twodimensional motion in an image sequence based on motion trajectories. First, a multiscale segmentation is performed to generate homogeneous regions in each frame. Regions between consecutive frames are then matched to obtain twoview correspondences. Affine transformations are computed from each pair of corresponding regions to define pixel matches. Pixels matches over consecutive image pairs are concatenated to obtain pixellevel motion trajectories across the image sequence. Motion patterns are learned from the extracted trajectories using a timedelay neural network. We apply the proposed method to recognize 40 hand gestures of American Sign Language. Experimental results show that motion patterns of hand gestures can be extracted and recognized accurately using motion trajectories. Index TermsÐMotion segmentation, motion analysis, motion trajectory, American Sign Language, hand gesture recognition, timedelay neural network. 1
Dynamic 3D Scene Analysis through Synthesis Feedback Control
, 1993
"... The analysis of 3D scenes consisting of nonrigid moving objects from 2D image sequences is discussed. A parametric description of dynamic objects is extracted and the time variant scene parameters are estimated throughout the sequence by employing an analysis by synthesis approach. From the parametr ..."
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Cited by 58 (9 self)
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The analysis of 3D scenes consisting of nonrigid moving objects from 2D image sequences is discussed. A parametric description of dynamic objects is extracted and the time variant scene parameters are estimated throughout the sequence by employing an analysis by synthesis approach. From the parametric scene description images are synthesized and compared with the original input images of the camera. Frame differences between both images are evaluated to estimate a scene parameter update. The analysis system is applied to video phone scenes as a data compression algorithm where the scene parameters are transmitted and the output sequence is synthesized at the receiver. Index Terms  3D scene analysis, dynamic scene analysis, image sequence analysis, 3D motion estimation, 3D object tracking, 3D shape reconstruction, 3D shape adaptation, analysis by synthesis, objectoriented video phone coding. I. INTRODUCTION Modelling of 3D scenes from 2D image sequences has been a research topic for...
Estimating Motion in Image Sequences  A tutorial on modeling and computation of 2D motion
 IEEE Signal Processing Magazine
, 1999
"... this paper should be helpful to researchers and practitioners working in the fields of video compression and processing, as well as in computer vision. Although the understanding of issues involved in the computation of motion has significantly increased over the last decade, we are still far from g ..."
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Cited by 45 (0 self)
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this paper should be helpful to researchers and practitioners working in the fields of video compression and processing, as well as in computer vision. Although the understanding of issues involved in the computation of motion has significantly increased over the last decade, we are still far from generic, robust, realtime motion estimation algorithms. The selection of the best motion estimator is still highly dependent on the application. Nevertheless, a broad variety of estimation models, criteria and optimization schemes can be treated in a unified framework presented here, thus allowing a direct comparison and leading to a deeper understanding of the properties of the resulting estimators.