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Epipolarplane image analysis: An approach to determining structure from motion
 INTERN..1. COMPUTER VISION
, 1987
"... We present a technique for building a threedimensional description of a static scene from a dense sequence of images. These images are taken in such rapid succession that they form a solid block of data in which the temporal continuity from image to image is approximately equal to the spatial conti ..."
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Cited by 255 (3 self)
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We present a technique for building a threedimensional description of a static scene from a dense sequence of images. These images are taken in such rapid succession that they form a solid block of data in which the temporal continuity from image to image is approximately equal to the spatial continuity in an individual image. The technique utilizes knowledge of the camera motion to form and analyze slices of this solid. These slices directly encode not only the threedimensional positions of objects, but also such spatiotemporal events as the occlusion of one object by another. For straightline camera motions, these slices have a simple linear structure that makes them easier to analyze. The analysis computes the threedimensional positions of object features, marks occlusion boundaries on the objects, and builds a threedimensional map of "free space." In our article, we first describe the application of this technique to a simple camera motion, and then show how projective duality is used to extend the analysis to a wider class of camera motions and object types that include curved and moving objects.
Bayesian Estimation Of Motion Vector Fields
 IEEE Trans. Pattern Anal. Machine Intell
, 1992
"... This paper presents a new approach to the estimation of twodimensional motion vector fields from timevarying images. The approach is stochastic, both in its formulation and in the solution method. The formulation involves the specification of a deterministic structural model, along with stochastic ..."
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Cited by 137 (19 self)
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This paper presents a new approach to the estimation of twodimensional motion vector fields from timevarying images. The approach is stochastic, both in its formulation and in the solution method. The formulation involves the specification of a deterministic structural model, along with stochastic observation and motion field models. Two motion models are proposed: a globally smooth model based on vector Markov random fields and a piecewise smooth model derived from coupled vectorbinary Markov random fields. Two estimation criteria are studied. In the Maximum A Posteriori Probability (MAP) estimation the a posteriori probability of motion given data is maximized, while in the Minimum Expected Cost (MEC) estimation the expectation of a certain cost function is minimized. The MAP estimation is performed via simulated annealing, while the MEC algorithm performs iterationwise averaging. Both algorithms generate sample fields by means of stochastic relaxation implemented via the Gibbs s...
Recursive 3D Motion Estimation from a Monocular Image Sequence
 IEEE Transactions on Aerospace and Electronic Systems
, 1990
"... The problem considered here involves the design and application of a recursive algorithm to a sequence of images of a moving object to estimate both its structure and kinematics. The object is assumed to be rigid, and its motion is assumed to be "smooth" in the sense that it can be modeled ..."
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Cited by 111 (1 self)
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The problem considered here involves the design and application of a recursive algorithm to a sequence of images of a moving object to estimate both its structure and kinematics. The object is assumed to be rigid, and its motion is assumed to be "smooth" in the sense that it can be modeled by retaining an arbitrary number of terms in the appropriate Taylor series expansions. Translational motion involves a standard rectilinear model, while rotational motion is described with quaternions. Neglected terms of the Taylor series are modeled as process noise. A statespace model is constructed, incorporating both kinematic and structural states, and recursive techniques are used to estimate the state vector as a function of time.
Optical flow estimation: an error analysis of gradientbased methods with local optimization
 IEEE Trans. PAMI
, 1987
"... AbstractMultiple views of a scene can provide important information about the structure and dynamic behavior of threedimensional objects. Many of the methods that recover this information require the determination of optical flowthe velocity, on the image, of visible points on object surfaces. An ..."
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Cited by 90 (1 self)
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AbstractMultiple views of a scene can provide important information about the structure and dynamic behavior of threedimensional objects. Many of the methods that recover this information require the determination of optical flowthe velocity, on the image, of visible points on object surfaces. An important class of techniques for estimating optical flow depend on the relationship between the gradients of image brightness. While gradientbased methods have been widely studied, little attention has been paid to accuracy and reliability of the approach. Gradientbased methods are sensitive to conditions commonly encountered in real imagery. Highly textured surfaces, large areas of constant brightness, motion boundaries, and depth discontinuities can all be troublesome for gradientbased methods. Fortunately, these problematic areas are usually localized can be identified in the image. In this paper we examine the sources of errors for gradientbased techniques that locally solve for optical flow. These methods assume that optical flow is constant in a small neighborhood. The consequence of violating in this assumption is examined. The causes of measurement errors and the determinants of the conditioning of the solution system are also considered. By understanding how errors arise, we are able to define the inherent limitations of the technique, obtain estimates of the accuracy of computed values, enhance the performance of the technique, and demonstrate the informative value of some types of error. Index TermsComputer vision, dynamic scene analysis, error analysis, motion, optical flow, timevarying imagery. 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 ...
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.
The Statistics of Optical Flow
 Computer Vision and Image Understanding
, 1999
"... When processing image sequences some representation of image motion must be derived as a first stage. The most often used such representation is the optical flow field, which is a set of velocity measurements of image patterns. It is well known that it is very difficult to estimate accurate optical ..."
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Cited by 39 (6 self)
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When processing image sequences some representation of image motion must be derived as a first stage. The most often used such representation is the optical flow field, which is a set of velocity measurements of image patterns. It is well known that it is very difficult to estimate accurate optical flow at locations in an image which correspond to scene discontinuities. What is less well known, however, is that even at the locations corresponding to smooth scene surfaces, the optical flow field often cannot be estimated accurately. Noise in the data causes many optical flow estimation techniques to give biased flow estimates. Very often there is consistent bias: the estimate tends to be an underestimate in length and to be in a direction closer to the majority of the gradients in the patch. This paper studies all three major categories of flow estimation methodsgradientbased, energybased, and correlation methods, and it analyzes different ways of compounding onedimensional motio...
From Surfaces to Objects: Computer Vision and ThreeDimensional Scene Analysis
, 1989
"... This book was originally published by John Wiley and Sons, ..."
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Cited by 38 (11 self)
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This book was originally published by John Wiley and Sons,
A Physiological Model for Motionstereo Integration and a Unified Explanation of the Pulfrichlike Phenomena
, 1997
"... Many psychophysical and physiological experiments indicate that visual motion analysis and stereoscopic depth perception are processed together in the brain. However, little computational effort has been devoted to combining these two visual modalities into a common framework based on physiological ..."
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Cited by 32 (11 self)
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Many psychophysical and physiological experiments indicate that visual motion analysis and stereoscopic depth perception are processed together in the brain. However, little computational effort has been devoted to combining these two visual modalities into a common framework based on physiological mechanisms. We present such an integrated model in this paper. We have previously developed a physiologically realistic model for binocular disparity computation (Qian, 1994). Here we demonstrate that under some general and physiological assumptions, our stereo vision model can be combined naturally with motion energy models to achieve motionstereo integration. The integrated model may be used to explain a wide range of experimental observations regarding motionstereo interaction. As an example, we show that the model can provide a unified account of the classical Pulfrich effect (Morgan and Thompson, 1975) and the generalized Pulfrich phenomena to dynamic noise patterns (Tyler, 1974; Falk,...
J: Estimation of 2D motion fields from image sequences with application to motioncompensated processing
 In Motion Analysis and Image Sequence Processing Edited by: Lagendijk MISRL
"... c ○ 1993 Kluwer Academic Publishers. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component ..."
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Cited by 32 (12 self)
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c ○ 1993 Kluwer Academic Publishers. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the Kluwer Academic Publishers. 3