Robust Estimation of Three-Dimensional Motion Parameters from a Sequence of Image Frames Using Regularization

Yasumoto, Yoshio; Medioni, Gérard

doi:10.1109/tpami.1986.4767810

Cited by 64 publications

(21 citation statements)

References 16 publications

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“…This conclusion is supported in recent computational studies (e.g., Adiv, 1985;Bhanu & Burger, 1988;Bharwani, Riseman, & Hanson, 1986;Bolles & Baker, 1985;Bruss & Horn, 1983;Landy, 1987;Lawton, 1983;Negahdaripour & Horn, 1985;Shariat & Price, 1986;Subbarao, 1986;Ullman, 1984;Waxman & Wohn, 1988;Yasumoto & Medioni, 1985).…”

Section: The Computation Of 3-d Structure From Motionmentioning

confidence: 54%

The perceptual buildup of three-dimensional structure from motion

Hildreth¹,

Grzywacz²,

Adelson³

et al. 1990

Perception & Psychophysics

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We present a set of psychophysical experiments that measure the accuracy of perceived threedimensional (3-D)structure derived from relative motion in the changing two-dimensional image. The experiments are motivated in part by a computational model proposed by Ullman (1984), called the incremental rigidity scheme, in which an accurate 3-D structure is built up incrementally, by considering images of moving objects over an extended time period. Our main conclusions are: First, the human visual system can derive an accurate model of the relative depths of moving points, even in the presence of noise in their image positions; second, the accuracy of the 3-D model improves with time, eventually reaching a plateau; and third, the 3-D structure currently perceived appears to depend on previous 3-D models. Through computer simulations, we relate the results of our psychophysical experiments with the predictions of Ullman's model.A valuable source of three-dimensional (3-D) information is provided by the relative motions of elements in the changing two-dimensional (2-D) image. The remarkable ability of the human visual system to recover 3-D structure from motion was explored in many early perceptual studies (e.g., Braunstein, 1976;Gibson & Gibson, 1957;Green, 1961;Johansson, 1973Johansson, , 1978Rogers & Graham, 1979;Ullman, 1979;Wallach & O'Connell, 1953;White & Mueser, 1960). These studies revealed that the human system can recover the structure of rigid and nonrigid objects, under perspective and orthographic projection, and in the absence of all other cues to 3-D structure. Early perceptual work was typically focused on the recovery of qualitative aspects of an object's structure, such as its apparent rigidity, volume, or coherence. More recently, studies have addressed, quantitatively, the accuracy of perceived 3-D structure (e.g., Braunstein, Hoffman, Shapiro, Andersen, & Bennett, 1987; Dosher, Landy, & Sperling, 1989;Lappin & Fuqua, 1983; Loomis & Eby, 1988 Sperling, Landy, Dosher, & Perkins, 1989;Todd, 1982Todd, , 1984Todd, , 1985.In this paper, we present a set of psychophysical experiments in which we have examined both the accuracy We thank Tomaso Poggio, Shimon Ullman, and Whitman Richards for valuable comments on this paper, and Mike Landy for useful discussions. This report describes research done within the Artificial In- The experiments were motivated in part by a computational model proposed by Ullman (1984), called the incremental rigidity scheme, in which an accurate 3-D structure is built up incrementally, as images of moving objects are considered over an extended time period. If this model captures some aspects of the human recovery of structure from motion, then two critical predictions arise. First, the accuracy of perceived structure should increase over an extended time period comparable to that of the model, and second, the current perception of structure should strongly influence later perceptions. Also, in contrast to previous structure-from-motion models, the incremental rigidity scheme e...

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Section: The Computation Of 3-d Structure From Motionmentioning

confidence: 54%

The perceptual buildup of three-dimensional structure from motion

Hildreth¹,

Grzywacz²,

Adelson³

et al. 1990

Perception & Psychophysics

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show abstract

“…The projections of ni-1 and ni onto a plane that is perpendicular to l are ni -(ni -l * I ) l and ni-(ni * 1 ) 1, respectively. The angle between these two projections gives the absolute value of /: cos--(I1i(n l)) (n *(n 1)l) (28) The sign of 0 is the same as the sign (ni-I x ni) * I (29) After l and 0 are found by (27), (28), and (29), R(l, 4) can be calculated by (24). R (mi -6, 0) and R (mi, 0) can be determined by (25) and (26): R(mi 1, 0) = R 1(1, 1) R(ni, Ai) (30) R(mi, 0) = R(ni, t i ) R-1(1, /).…”

Section: Continuous Precession and Discrete Two-view Motionmentioning

confidence: 99%

“…Let the precession angular frequency determined from (28) and (29) The variances of samples in the summations of (37), (38), and (39) as well as the residuals in (35) and (14) indicate the accuracy of the model for the current set of frames. They also depend on the noise level.…”

Section: (31)mentioning

confidence: 99%

“…Broida and Chellappa [4] discuss the inference of 2-D motion from 1-D image sequences under the assumption that the object undergoes constant translation and rotational 2-D motion and the structure of the object (the 3-D coordinates of the object points in the object-centered coordinate frame) is known. Yasumoto and Medioni [28] also assume the motion to be constant through the sequence and estimate, through a search in the solution space, the parameters of the assumed constant motion from image sequence. In the field of astrodynamics, the dynamic information about the object is required to be known.…”

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confidence: 99%

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3-D Motion Estimation, Understanding, and Prediction from Noisy Image Sequences

Weng

Huang

Ahuja

1987

IEEE Trans. Pattern Anal. Mach. Intell.

133

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Abstract-This paper presents an approach to understanding general 3-D motion of a rigid body from image sequences. Based on dynamics, a locally constant angular momentum (LCAM) model is introduced. The model is local in the sense that it is applied to a limited number of image frames at a time. Specifically, the model constrains the motion, over a local frame subsequence, to be a superposition of precession and translation. Thus, the instantaneous rotation axis of the object is allowed to change through the subsequence. The trajectory of the rotation center is approximated by a vector polynomial. The parameters of the model evolve in time so that they can adapt to long term changes in motion characteristics.The nature and parameters of short term motion can be estimated continuously with the goal of understanding motion through the image sequence. The estimation algorithm presented in this paper is linear, i.e., the algorithm consists of solving simultaneous linear equations. Based on the assumption that the motion is smooth, object positions and motion in the near future can be predicted, and short missing subsequences can be recovered.Noise smoothing is achieved by overdetermination and a leastsquares criterion. The framework is flexible in the sense that it allows both overdetermination in number of feature points and the number of image frames. The number of frames from which the model is derived can be varied according to the complexity of motion and the noise level so as to obtain stable and good estimates of parameters over the entire image sequence. Simulation results are given for noisy synthetic data and images taken of a model airplane.

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“…Surface normal vectors n of the object are obtained by differentiating the depth spatially. An algorithm for estimating depth from motion field was proposed by Yasumoto and Medioni [6] and in this paper we apply their algorithm.…”

Section: )mentioning

confidence: 99%

Estimation of light source information from image sequence

Mukawa

1992

Systems & Computers in Japan

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SUMMARYThis paper proposes an algorithm that analym shading scenes with motion based on a shading model and estimates an incident direction of a light source illuminating an object. If the incident direction of a light source can be estimated. this means that an important environmental parameter of a scene is obtained which enables precise estimation of such parameters as shape and motion in the scene. Computer simulations have been performed for computer-generated images and real images, and it was verified that the algorithm works correctly.

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Robust Estimation of Three-Dimensional Motion Parameters from a Sequence of Image Frames Using Regularization

Cited by 64 publications

References 16 publications

The perceptual buildup of three-dimensional structure from motion

The perceptual buildup of three-dimensional structure from motion

3-D Motion Estimation, Understanding, and Prediction from Noisy Image Sequences

Estimation of light source information from image sequence

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