The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
The topological approach to perceptual organization
Abstract
To address the fundamental question of “what are the primitives of visual perception”, a theory of topological structure and functional hierarchy in visual perception has been proposed. This holds that the global nature of perceptual organization can be described in terms of topological invariants, global topological perception is prior to the perception of other featural properties, and the primitives of visual form perception are geometric invariants at different levels of structural stability. In Part I of this paper, I will illustrate why and how the topological approach to perceptual organization has been advanced. In Part II, I will provide empirical evidence supporting the early topological perception, while answering some commonly considered counteraccounts. In Part III, to complete the theory, I will apply the mathematics of tolerance spaces to describe global properties in discrete sets. In Part IV, I will further present experimental data to demonstrate the global-to-local functional hierarchy in form perception, which is stratified with respect to structural stability defined by Klein's Erlangen Program. Finally, in Part V, I will discuss relations of the global-to-local topological model to other theories: The topological approach reformulates both classical Gestalt holism and Gibson's direct perception of invariance, while providing a challenge to computational approaches to vision based on the local-to-global assumption.
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Cites
The Ecological Approach to Visual Perception
Bornstein, M. H., & Gibson, J. J. (1980) · The Journal of Aesthetics and Art Criticism · doi:10.2307/429816 · Cited by 22,935
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686
A feature-integration theory of attention.
Treisman, A. M., & Gelade, G. (1980) · Cognitive Psychology · doi:10.1016/0010-0285(80)90005-5 · Cited by 12,366
Neural Mechanisms of Selective Visual Attention
Desimone, R., & Duncan, J. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.001205 · Cited by 8,388
Receptive fields and functional architecture of monkey striate cortex
Hubel, D. H., & Wiesel, T. N. (1968) · The Journal of Physiology · doi:10.1113/jphysiol.1968.sp008455 · Cited by 6,568
Marr, D. (1982) · Cumulative Index of Computer Aided Architectural Design · Cited by 5,733
Barlow, H. B. (1983) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(83)90030-5 · Cited by 5,424
(1982) · Elsevier eBooks · doi:10.1016/c2009-0-21955-6 · Cited by 4,596
Ungerleider, L. G. (1982) · Medical Entomology and Zoology · Cited by 4,531
Forest before trees: The precedence of global features in visual perception
Navon, D. (1977) · Cognitive Psychology · doi:10.1016/0010-0285(77)90012-3 · Cited by 4,005
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Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., & Gong, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01220 · Cited by 499
GREENE, M., & OLIVA, A. (2009) · Cognitive Psychology · doi:10.1016/j.cogpsych.2008.06.001 · Cited by 467
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Metadata
- Authors
- Chen, L.
- Venue
- Visual Cognition
- Publisher
- Informa UK Limited
- Published
- 2005-05
- Volume
- 12
- Issue
- 4
- Pages
- 553-637
- ISSN
- 1350-6285 (ISSN-L)
- 1464-0716
- OpenAlex
- W2030503833
- Cited by
- 260
- References
- 80
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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