The feasibility of motion and structure from noisy time-varying image velocity information

Barron, John A.; Jepson, Allan D.; Tsotsos, John K.

doi:10.1007/bf00126501

Cited by 35 publications

(12 citation statements)

References 33 publications

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“…An advantage of these methods is that the motion in each patch is computed independently, so the methods can deal with multiple moving objects. A problem with these methods is that they are sensitive to errors in the flow-field measurements, particularly for small patches (see Waxman and Wohn [1988] and Barron et al [1990]). …”

Section: Instantaneous-eme Algorithmsmentioning

confidence: 99%

Subspace methods for recovering rigid motion I: Algorithm and implementation

Heeger

Jepson

1992

Int J Comput Vision

Self Cite

379

241

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As an observer moves and explores the environment, the visual stimulation in his/her eye is constantly changing. Somehow he/she is able to perceive the spatial layout of the scene, and to discern his/her movement through space. Computational vision researchers have been trying to solve this problem for a number of years with only limited success. It is a difficult problem to solve because the optical flow field is nonlinearly related to the 3D motion and depth parameters.Here, we show that the nonlinear equation describing the optical flow field can be split by an exact algebraic manipulation to form three sets of equations. The first set relates the flow field to only the translational component of 3D motion. Thus, depth and rotation need not be known or estimated prior to solving for translation. Once the translation has been recovered, the second set of equations can be used to solve for rotation. Finally, depth can be estimated with the third set of equations, given the recovered translation and rotation.The algorithm applies to the general case of arbitrary motion with respect to an arbitrary scene. It is simple to compute, and it is plausible biologically. The results reported in this article demonstrate the potential of our new approach, and show that it performs favorably when compared with two other well-known algorithms.

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Section: Instantaneous-eme Algorithmsmentioning

confidence: 99%

Subspace methods for recovering rigid motion I: Algorithm and implementation

Heeger

Jepson

1992

Int J Comput Vision

Self Cite

379

241

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“…Fortunately, in most vision applications a crude degree of separation is sufficient in that the occurrence of more than two or three velocities in a small neighborhood is unlikely. This also means, however, that a subsequent stage of processing is required because the accuracy required for tasks such as the determination of ego-motion and surface parameters is greater than the tuning width of single filters (Barron 1988). Previous frequency-based approaches toward this end have been amplitude-based, and have sacrificed velocity resolution as a consequence of using the relative amplitudes of differently tuned f'flters (Adelson and Bergen 1986;Heeger 1987Heeger , 1988.…”

Section: Introductionmentioning

confidence: 99%

Computation of component image velocity from local phase information

Fleet

Jepson

1990

Int J Comput Vision

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901

515

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We present a technique for the computation of 2D component velocity from image sequences. Initially, the image sequence is represented by a family of spatiotemporal velocity-tuned linear filters. Component velocity, computed from spatiotemporal responses of identically tuned filters, is expressed in terms of the local first-order behavior of surfaces of constant phase. Justification for this definition is discussed from the perspectives of both 2D image translation and deviations from translation that are typical in perspective projections of 3D scenes. The resulting technique is predominantly linear, efficient, and suitable for parallel processing. Moreover, it is local in space-time, robust with respect to noise, and permits multiple estimates within a single neighborhood. Promising quantitative results are reported from experiments with realistic image sequences, including cases with sizeable perspective deformation. I IntroductionThis article addresses the quantitative measurement of velocity in image sequences. The important issues are (1) the accuracy with which velocity can be computed;(2) robustness with respect to smooth contrast variations and affine deformation (i.e., deviations from 2D image translation that are typical in perspective projections of 3D scenes); (3) localization in space-time; (4) noise robustness; and (5) the ability to discern different velocities within a single neighborhood. Our approach is based on the phase information in a local-frequency representation of the image sequence that is produced by a family of velocity-tuned linear filters. The velocity measurements are limited to component velocity: the projected components of 2D velocity onto directions normal to oriented structure in the image (a definition is given in section 3). The combination of these measurements to derive the full 2D velocity is briefly discussed.Our reasons for concentrating on component velocity (also referred to as normal velocity) stem from a desire for local measurements, and the well-known aperture problem (Mart and Ullman 1981). Local measurements allow smoothly varying velocity fields to be estimated based on translational image velocity as opposed to more complicated descriptions of the velocity field over larger image patches. However, in narrow spatiotemporal apertures the intensity structure is often roughly one-dimensional so that only one component of the image velocity can be accurately determined. To obtain full 2D velocity fields, larger space-time support is therefore required. In our view, the common assumptions of smoothness, uniqueness, and the coherence of neighboring measurements that are involved in combining local measurements to determine 2D velocity, to fill in regions without measurements, and to reduce the effects of noise, should be viewed as aspects of interpretation, and as such, are distinct issues. In considering just normal components of velocity we hope to obtain more accurate estimates of motion within smaller apertures, which leads to better spatial resolution of veloci...

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“…Since it is impossible to determine absolute values of the translation and depth using monocular schemes, 3D interpretation can only be achieved by applying an arbitrary scale factor to the relative 0020-0255/$ -see front matter Ó 2007 Elsevier Inc. All translational motion and depth values [1]. Secondly, virtually none of the parameter reconstruction techniques presented in the literature provide reliable results when applied to the optical flow fields calculated from realistic scenes due to the difficulties involved in extracting accurate flow fields [6]. Thirdly, most parameter estimation algorithms designed to solve equations of motion are characterized by some form of nonlinearity.…”

Section: Introductionmentioning

confidence: 97%

“…However, a number of major problems exist in the 3D motion parameter estimation field. Firstly, while monocular observers designed to visualize relative motion within a scene have the benefit of an extremely simple hardware structure, they are unable to recover translational motion parameters or the coordinates of 3D structures with any degree of reliability due to their inherent depth-speed ambiguity [6]. Since it is impossible to determine absolute values of the translation and depth using monocular schemes, 3D interpretation can only be achieved by applying an arbitrary scale factor to the relative 0020-0255/$ -see front matter Ó 2007 Elsevier Inc. All translational motion and depth values [1].…”

Section: Introductionmentioning

confidence: 99%