The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Perceptual organization and the representation of natural form
Open accessAbstract
To support our reasoning abilities perception must recover environmental regularities—e.g., rigidity, “objectness,” axes of symmetry—for later use by cognition. To create a theory of how our perceptual apparatus can produce meaningful cognitive primitives from an array of image intensities we require a representation whose elements may be lawfully related to important physical regularities, and that correctly describes the perceptual organization people impose on the stimulus. Unfortunately, the representations that are currently available were originally developed for other purposes (e.g., physics, engineering) and have so far proven unsuitable for the problems of perception or common-sense reasoning. In answer to this problem we present a representation that has proven competent to accurately describe an extensive variety of natural forms (e.g., people, mountains, clouds, trees), as well as man-made forms, in a succinct and natural manner. The approach taken in this representational system is to describe scene structure at a scale that is similar to our naive perceptual notion of “a part,” by use of descriptions that reflect a possible formative history of the object, e.g., how the object might have been constructed from lumps of clay. For this representation to be useful it must be possible to recover such descriptions from image data; we show that the primitive elements of such descriptions may be recovered in an overconstrained and therefore reliable manner. We believe that this descriptive system makes an important contribution towards solving current problems in perceiving and reasoning about natural forms by allowing us to construct accurate descriptions that are extremely compact and that capture people's intuitive notions about the part structure of three-dimensional forms.
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Cites
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
The psychology of computer vision
Rosenfeld, A. (1976) · Artificial Intelligence · doi:10.1016/0004-3702(76)90008-4 · Cited by 2,966
Cognitive Development and Acquisition of Language
(1973) · Elsevier eBooks · doi:10.1016/c2009-0-22101-5 · Cited by 2,193
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
Fractal-Based Description of Natural Scenes
Pentland, A. P. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767591 · Cited by 1,800
Self-citationMACHINE PERCEPTION OF THREE-DIMENSIONAL SOLIDS,
Roberts, L. G. (1963) · Defense Technical Information Center (DTIC) · Cited by 1,551
Laws of organization in perceptual forms.
Wertheimer, M. (1938) · A source book of Gestalt psychology. · doi:10.1037/11496-005 · Cited by 1,493
ROSCH, E. H. (1973) · Cognitive Development and Acquisition of Language · doi:10.1016/b978-0-12-505850-6.50010-4 · Cited by 1,369
Superquadrics and Angle-Preserving Transformations
Barr (1981) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.1981.1673799 · Cited by 1,100
Particle Systems—a Technique for Modeling a Class of Fuzzy Objects
Reeves, W. T. (1983) · ACM Transactions on Graphics · doi:10.1145/357318.357320 · Cited by 1,078
Cited by
Fitting parameterized three-dimensional models to images
Lowe, D. G. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.134043 · Cited by 837
Norman, J. (2002) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0200002x · Cited by 466
Good vibrations: modal dynamics for graphics and animation
Pentland, A., & Williams, J. (1989) · Proceedings of the 16th annual conference on Computer graphics and interactive techniques · doi:10.1145/74333.74355 · Cited by 389
Self-citation50 Years of object recognition: Directions forward
Andreopoulos, A., & Tsotsos, J. K. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.04.005 · Cited by 347
Retrieving articulated 3-D models using medial surfaces
Siddiqi, K., Zhang, J., Macrini, D., Shokoufandeh, A., Bouix, S., & Dickinson, S. (2008) · Machine Vision and Applications · doi:10.1007/s00138-007-0097-8 · Cited by 256
Metadata
- Authors
- Pentland, A. P.
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 1986-05
- Volume
- 28
- Issue
- 3
- Pages
- 293-331
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2039005177
- Cited by
- 561
- References
- 30
- FWCI
- 32.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
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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.