Techniques for disparity measurement

Many different approaches have been suggested for the measurement of structure in space from spatially separated cameras. In this report we critically examine some of these techniques. Through a series of examples we show that none of the current mechanisms of disparity measurement are particularly robust. By considering some of the implications of disparity in the frequency domain, we present a new definition of disparity that is tied to the interocular phase difference in bandpass versions of the monocular images. Finally, we present a new technique for measuring disparity as the local phase difference between bandpass versions of the two images, and we show how this technique surmounts some of the difficulties encountered by current disparity detection mechanisms.

[1]  J P Frisby,et al.  The Computation of Binocular Edges , 1980, Perception.

[2]  Michael Jenkin,et al.  Some Problems with Correspondence , 1988 .

[3]  C. Blakemore,et al.  The neural mechanism of binocular depth discrimination , 1967, The Journal of physiology.

[4]  D. Hubel,et al.  Stereoscopic Vision in Macaque Monkey: Cells sensitive to Binocular Depth in Area 18 of the Macaque Monkey Cortex , 1970, Nature.

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

[6]  Michael A Crombie.  Coordination of Stereo Image Registration and Pixel Classification , 1982 .

[7]  D Marr,et al.  Cooperative computation of stereo disparity. , 1976, Science.

[8]  C. Blakemore,et al.  A second neural mechanism of binocular depth discrimination , 1972, The Journal of physiology.

[9]  C. D. Kuglin,et al.  The phase correlation image alignment method , 1975 .

[10]  Charles E. Thorpe,et al.  An analysis of interest operators for FIDO , 1983 .

[11]  Demetri Terzopoulos.  Multi-Level Reconstruction of Visual Surfaces: Variational Principles and Finite Element Representations , 1982 .

[12]  John E. W. Mayhew,et al.  Psychophysical and Computational Studies Towards a Theory of Human Stereopsis , 1981, Artif. Intell..

[13]  Richard M. Stern,et al.  Fast Computation of the Difference of Low-Pass Transform , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Tomaso Poggio,et al.  A Theory of Human Stereo Vision , 1977 .

[15]  J P Frisby,et al.  PMF: A Stereo Correspondence Algorithm Using a Disparity Gradient Limit , 1985, Perception.

[16]  John K. Tsotsos,et al.  Visual stereoscopic computation , 1988 .

[17]  H. K. Nishihara,et al.  CARTOON: A Biologically Motivated Edge Detection Algorithm , 1982 .

[18]  A.D. Jepson,et al.  The fast computation of disparity from phase differences , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Hans P. Morevec.  Towards automatic visual obstacle avoidance , 1977, IJCAI 1977.

[20]  R. Kelly,et al.  The Gestalt Photomapping System , 1977 .

[21]  Berthold K. P. Horn.  Non-correlation methods for stereo matching , 1983 .

[22]  G. Poggio,et al.  Binocular interaction and depth sensitivity in striate and prestriate cortex of behaving rhesus monkey. , 1977, Journal of neurophysiology.

[23]  Eric L. W. Grimson,et al.  From Images to Surfaces: A Computational Study of the Human Early Visual System , 1981 .

[24]  David J. Fleet,et al.  The Extraction of Orientation and 2-D Velocity through Hierarchical Processing , 1986, Other Conferences.

[25]  P. J. Burt,et al.  Fast Filter Transforms for Image Processing , 1981 .

[26]  C. Rapley,et al.  Absorption of soft X rays by material within Gould's Belt , 1975, Nature.

[27]  William B. Thompson,et al.  Disparity Analysis of Images , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Hans P. Moravec.  Towards Automatic Visual Obstacle Avoidance , 1977, IJCAI.