A Probabilistic Model for Recovering Camera Translation

This paper describes the mathematical basis and application of a probabilistic model for recovering the direction of camera translation (heading) from optical flow. According to the theorem that heading cannot lie between two converging points in a stationary environment, one can compute the posterior probability distribution of heading across the image and choose the heading with maximum a posteriori (MAP). The model requires very simple computation, provides confidence level of the judgments, applies to both linear and curved trajectories, functions in the presence of camera rotations, and exhibited high accuracy up to 0.1°?0.2° in random dot simulations.

[1]  Nicholas G. Hatsopoulos,et al.  Visual navigation with a neural network , 1991, Neural Networks.

[2]  Jan J. Koenderink,et al.  Local structure of movement parallax of the plane , 1976 .

[3]  Pascal Mamassian,et al.  Observer biases in the 3D interpretation of line drawings , 1998, Vision Research.

[4]  Ranxiao Frances Wang,et al.  Human heading judgments and object-based motion information , 1999, Vision Research.

[5]  Constance S. Royden,et al.  Analysis of misperceived observer motion during simulated eye rotations , 1994, Vision Research.

[6]  Berthold K. P. Horn,et al.  "Determining optical flow": A Retrospective , 1993, Artif. Intell..

[7]  Ranxiao Frances Wang,et al.  Where we Go With a Little Good Information , 1999 .

[8]  David C. Knill,et al.  Learning a Near-Optimal Estimator for Surface Shape from Shading , 1990, Comput. Vis. Graph. Image Process..

[9]  J. Koenderink,et al.  Exterospecific component of the motion parallax field. , 1981, Journal of the Optical Society of America.

[10]  Paul A. Braren,et al.  Wayfinding on foot from information in retinal, not optical, flow. , 1992, Journal of experimental psychology. General.

[11]  A. W. Blackwell,et al.  Perception of circular heading from optical flow. , 1991, Journal of experimental psychology. Human perception and performance.

[12]  M. Banks,et al.  Perceiving heading with different retinal regions and types of optic flow , 1993, Perception & psychophysics.

[13]  Jan J. Koenderink,et al.  Detection of first-order structure in optic flow fields , 1996, Vision Research.

[14]  Daniel J. Hannon,et al.  Direction of self-motion is perceived from optical flow , 1988, Nature.

[15]  Michael I. Jordan,et al.  Obstacle Avoidance and a Perturbation Sensitivity Model for Motor Planning , 1997, The Journal of Neuroscience.

[16]  J E Cutting,et al.  Wayfinding, displacements, and mental maps: velocity fields are not typically used to determine one's aimpoint. , 1995, Journal of experimental psychology. Human perception and performance.

[17]  J H Rieger,et al.  Processing differential image motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[18]  Ellen C. Hildreth,et al.  Recovering heading for visually-guided navigation , 1992, Vision Research.

[19]  James E. Cutting,et al.  Perception with an eye for motion , 1986 .

[20]  W H Warren,et al.  Perceiving Heading in the Presence of Moving Objects , 1995, Perception.

[21]  William T. Freeman,et al.  The generic viewpoint assumption in a framework for visual perception , 1994, Nature.

[22]  Berthold K. P. Horn.  Robot vision , 1986, MIT electrical engineering and computer science series.

[23]  W. Warren,et al.  Perception of translational heading from optical flow. , 1988, Journal of experimental psychology. Human perception and performance.

[24]  Ellen C. Hildreth,et al.  Measurement of Visual Motion , 1984 .

[25]  D H Owen,et al.  The utility of motion parallax information for the perception and control of heading. , 1999, Journal of experimental psychology. Human perception and performance.

[26]  Dean H. Owen,et al.  The utility of motion parallax information for the perception and control of heading. , 1999, Journal of experimental psychology. Human perception and performance.

[27]  James E. Cutting,et al.  Wayfinding from multiple sources of local information in retinal flow. , 1996 .

[28]  J E Cutting,et al.  Heading and path information from retinal flow in naturalistic environments , 1997, Perception & psychophysics.

[29]  A. Remole.  PERCEPTION WITH AN EYE FOR MOTION , 1987 .

[30]  Edward H. Adelson,et al.  A unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of models , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  H. C. Longuet-Higgins,et al.  The interpretation of a moving retinal image , 1980, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[32]  D Regan,et al.  How do we avoid confounding the direction we are looking and the direction we are moving? , 1982, Science.

[33]  Constance S. Royden,et al.  Human heading judgments in the presence of moving objects , 1996, Perception & psychophysics.

[34]  J. Perrone,et al.  A model of self-motion estimation within primate extrastriate visual cortex , 1994, Vision Research.

[35]  M. Landy,et al.  Measurement and modeling of depth cue combination: in defense of weak fusion , 1995, Vision Research.

[36]  D C Knill,et al.  Perception of surface contours and surface shape: from computation to psychophysics. , 1992, Journal of the Optical Society of America. A, Optics and image science.