Binocular motion stereo using MAP estimation

An algorithm for fusing monocular and stereo cues to get robust estimates of both motion and structure is presented. The authors' algorithm assumes the motion to be along a smooth trajectory and the sequence of images to be dense. The algorithm starts by calculating the instantaneous FOE (focus of expansion). Knowing the FOE, a MAP estimate of the displacement at each pixel and an associated confidence measure are calculated. Using the displacement estimates, a relative depth map is calculated from one of the two frame sequences. By calculating the disparities at some feature points and using information about their relative depths, the instantaneous component of velocity in the direction perpendicular to the image plane (the Z direction) is computed. Using this information, a depth map is calculated; this depth map is then used to derive a prior probability distribution for disparity that is used in matching the two frames of the stereo pairs. This method is used to estimate the disparity of each pixel independently. Experimental results on a real image sequence are given.<<ETX>>

[1]  T. Poggio,et al.  A computational theory of human stereo vision , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[2]  Ramakant Nevatia,et al.  Segment-based stereo matching , 1985, Comput. Vis. Graph. Image Process..

[3]  Davi Geiger,et al.  STEREOPSIS AND EYE-MOVEMENT. , 1987 .

[4]  W. Eric L. Grimson,et al.  Computational Experiments with a Feature Based Stereo Algorithm , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[6]  Masatoshi Okutomi,et al.  A Bayesian Foundation for Active Stereo Vision1 , 1990, Other Conferences.

[7]  Takeo Kanade,et al.  Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Gilad Adiv,et al.  Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Olivier D. Faugeras,et al.  Building, Registrating, and Fusing Noisy Visual Maps , 1988, Int. J. Robotics Res..

[10]  Hans P. Moravec Obstacle avoidance and navigation in the real world by a seeing robot rover , 1980 .

[11]  Thomas O. Binford,et al.  Depth from Edge and Intensity Based Stereo , 1981, IJCAI.