The DOI matches its Crossref record.
Binocular Depth Perception of Computer-Generated Patterns
Abstract
The perception of depth involves monocular and binocular depth cues. The latter seem simpler and more suitable for investigation. Particularly important is the problem of finding binocular parallax, which involves matching patterns of the left and right visual fields. Stereo pictures of familiar objects or line drawings preclude the separation of interacting cues, and thus this pattern-matching process is difficult to investigate. More insight into the process can be gained by using unfamiliar picture material devoid of all cues except binocular parallax. To this end, artificial stereo picture pairs were generated on a digital computer. When viewed monocularly, they appear completely random, but if viewed binocularly, certain correlated point domains are seen in depth. By introducing distortions in this material and testing for perception of depth, it is possible to show that pattern-matching of corresponding points of the left and right visual fields can be achieved by first combining the two fields and then searching for patterns in the fused field. By this technique, some interesting properties of this fused binocular field are revealed, and a simple analog model is derived. The interaction between the monocular and binocular fields is also describea. A number of stereo images that demonstrate these and other findings are presented.
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Cited by
Minimal theory of 3D vision: New approach to visual scale and visual shape.
Linton, P. (2023) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2021.0455 · Cited by 13
Metadata
- Authors
- Julesz, B.
- Venue
- Bell System Technical Journal
- Published
- 1960-09
- Volume
- 39
- Issue
- 5
- Pages
- 1125-1162
- ISSN
- 0005-8580 (ISSN-L)
- OpenAlex
- W2111717646
- Cited by
- 731
- References
- 0
- FWCI
- 3.39
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.