The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Stereoscopic Depth: Its Relation to Image Segmentation, Grouping, and the Recognition of Occluded Objects
Abstract
Image regions corresponding to partially hidden objects are enclosed by two types of bounding contour: those inherent to the object itself (intrinsic) and those defined by occlusion (extrinsic). Intrinsic contours provide useful information regarding object shape, whereas extrinsic contours vary arbitrarily depending on accidental spatial relationships in scenes. Because extrinsic contours can only degrade the process of surface description and object recognition, it is argued that they must be removed prior to a stage of template matching. This implies that the two types of contour must be distinguished relatively early in visual processing and we hypothesize that the encoding of depth is critical for this task. The common border is attached to and regarded as intrinsic to the closer region, and detached from and regarded as extrinsic to the farther region. We also suggest that intrinsic borders aid in the segmentation of image regions and thus prevent grouping, whereas extrinsic borders provide a linkage to other extrinsic borders and facilitate grouping. Support for these views is found in a series of demonstrations, and also in an experiment where the expected superiority of recognition was found when partially sampled faces were seen in a back rather than a front stereoscopic depth plane.
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Cites
Representation and recognition of the spatial organization of three-dimensional shapes
Marr, D., & Nishihara, H. K. (1978) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1978.0020 · Cited by 2,158
Pittenger, J. B., Kubovy, M., & Pomerantz, J. R. (1982) · The American Journal of Psychology · doi:10.2307/1422146 · Cited by 1,415
Hoffman, D. D., & Richards, W. A. (1984) · Cognition · doi:10.1016/0010-0277(84)90022-2 · Cited by 1,342
Organization in vision: Essays on gestalt perception
Kanizsa, G. (1979) · Medical Entomology and Zoology · Cited by 924
Serial and parallel processing of visual feature conjunctions
Nakayama, K., & Silverman, G. H. (1986) · Nature · doi:10.1038/320264a0 · Cited by 863
Self-citationTs'o, D., Gilbert, C., & Wiesel, T. (1986) · The Journal of Neuroscience · doi:10.1523/jneurosci.06-04-01160.1986 · Cited by 842
Neural dynamics of perceptual grouping: Textures, boundaries, and emergent segmentations
Grossberg, S., & Mingolla, E. (1985) · Perception & Psychophysics · doi:10.3758/bf03198851 · Cited by 815
Laminar differences in receptive field properties of cells in cat primary visual cortex
Gilbert, C. D. (1977) · The Journal of Physiology · doi:10.1113/jphysiol.1977.sp011863 · Cited by 675
Human image understanding: Recent research and a theory
Biederman, I. (1985) · Computer Vision, Graphics, and Image Processing · doi:10.1016/0734-189x(85)90002-7 · Cited by 606
The organization of chromatic and spatial interactions in the primate striate cortex
Ts'o, D., & Gilbert, C. (1988) · The Journal of Neuroscience · doi:10.1523/jneurosci.08-05-01712.1988 · Cited by 548
Cited by
Wagemans, J., Elder, J. H., Kubovy, M., Palmer, S. E., Peterson, M. A., Singh, M., & von der Heydt, R. (2012) · Psychological Bulletin · doi:10.1037/a0029333 · Cited by 1,539
Contextual modulation in primary visual cortex
Zipser, K., Lamme, V. A. F., & Schiller, P. H. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-22-07376.1996 · Cited by 873
The neuropsychology of 3-D space.
Previc, F. H. (1998) · Psychological Bulletin · doi:10.1037/0033-2909.124.2.123 · Cited by 604
Deep problems with neural network models of human vision
Bowers, J. S., Malhotra, G., Dujmović, M., Llera Montero, M., Tsvetkov, C., Biscione, V., Puebla, G., Adolfi, F., Hummel, J. E., Heaton, R. F., et al. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x22002813 · Cited by 204
Metadata
- Authors
- Nakayama, K., Shimojo, S., & Silverman, G. H.
- Venue
- Perception
- Publisher
- SAGE Publications
- Published
- 1989-02
- Volume
- 18
- Issue
- 1
- Pages
- 55-68
- ISSN
- 0301-0066 (ISSN-L)
- 1468-4233
- OpenAlex
- W2093540385
- Cited by
- 398
- References
- 16
- FWCI
- 4.77
- Type
- journal article
- Language
- en
- Open access
- closed
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