Determining three-dimensional shape from orientation and spatial frequency disparities
Binocular differences in orientation and foreshortening are systematically related to surface slant and tilt and could potentially be exploited by biological and machine vision systems. Indeed, human stereopsis may possess a mechanism that specifically makes use of these orientation and spatial frequency disparities, in addition to the usual cue of horizontal disparity. In machine vision algorithms, orientation and spatial frequency disparities are a source of error in finding stereo correspondence because one seeks to find features or areas which are similar in the two views when, in fact, they are systematically different. In other words, it is common to treat as noise what is useful signal.
We have been developing a new stereo algorithm based on the outputs of linear spatial filters at a range of orientations and scales. We present a method in this framework, making use of orientation and spatial frequency disparities, to directly recover local surface slant. An implementation of this method has been tested on curved surfaces and quantitative experiments show that accurate surface orientation can be recovered efficiently. This method does not require the explicit identification of oriented line elements and also provides an explanation of the intriguing perception of surface slant in the presence of orientation or spatial frequency disparities, but in the absence of systematic positional correspondence.
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- Arnold RD, Binford TO (1980) Geometric constraints on stereo vision. Proc SPIE 238:281–292Google Scholar
- Burt P, Julesz B (1980) A disparity gradient limit for binocular function. Science 208:651–657Google Scholar
- Jones DG (1991) Computational models of binocular vision. PhD Thesis, Stanford UnivGoogle Scholar
- Jones DG, Malik J (1991) Determining three-dimensional shape from orientation and spatial frequency disparities I — using corresponding line elements. Technical Report UCB-CSD 91-656, University of California, BerkeleyGoogle Scholar
- Jones DG, Malik J (1992) A computational framework for determining stereo correspondence from a set of linear spatial filters. Proc ECCV GenovaGoogle Scholar
- Julesz B (1960) Binocular depth perception of computer generated patterns. Bell Syst Tech J 39:1125–1162Google Scholar
- Julesz B (1971) Foundations of cyclopean perception. University of Chicago Press:ChicagoGoogle Scholar
- Kass M (1983) Computing visual correspondence. DARPA IU Workshop 54–60Google Scholar
- Kass M (1988) Linear image features in stereopsis. Int J Computer Vision 357–368Google Scholar
- Mori K, Kododi M, Asada H (1973) An iterative prediction and correction method for automatic stereo comparison. Computer Graphics and Image Processing 2:393–401Google Scholar
- Quam LH (1984) Hierarchical warp stereo. Proc Image Understanding Workshop.Google Scholar
- Rogers BJ, Cagenello RB (1989) Orientation and curvature disparities in the perception of 3-D surfaces. Invest Ophth and Vis Science (suppl) 30:262Google Scholar
- von der Heydt R, Hänny P, Dursteller MR (1981) The role of orientation disparity in stereoscopic perception and the development of binocular correspondence. in Advances in Physiological Science: 16:461–470 Graystan E, Molnar P (eds) Oxford:PergammonGoogle Scholar
- Wildes RP (1991) Direct recovery of three-dimensional scene geometry from binocular stereo disparity. IEEE Trans PAMI 3(8):761–774Google Scholar
- Witkin AP, Terzopoulos D, Kass M (1987) Signal matching through scale space. Int J Computer Vision 1(2):133–144Google Scholar