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Representation and recognition of the spatial organization of three-dimensional shapes
Open accessAbstract
The human visual process can be studied by examining the computational problems associated with deriving useful information from retinal images. In this paper, we apply this approach to the problem of representing three-dimensional shapes for the purpose of recognition. 1. Three criteria, accessibility, scope and uniqueness, and stability and sensitivity, are presented for judging the usefulness of a representation for shape recognition. 2. Three aspects of a representation’s design are considered, (i) the representation’s coordinate system, (ii) its primitives, which are the primary units of shape information used in the representation, and (iii) the organization the representation imposes on the information in its descriptions. 3. In terms of these design issues and the criteria presented, a shape representation for recognition should: (i) use an object-centred coordinate system, (ii) include volumetric primitives of varied sizes, and (iii) have a modular organization. A representation based on a shape’s natural axes (for example the axes identified by a stick figure) follows directly from these choices. 4. The basic process for deriving a shape description in this representation must involve: (i) a means for identifying the natural axes of a shape in its image and (ii) a mechanism for transforming viewer-centred axis specifications to specifications in an object-centred coordinate system. 5. Shape recognition involves: (i) a collection of stored shape descriptions, and (ii) various indexes into the collection that allow a newly derived description to be associated with an appropriate stored description. The most important of these indexes allows shape recognition to proceed conservatively from the general to the specific based on the specificity of the information available from the image. 6. New constraints supplied by a conservative recognition process can be used to extract more information from the image. A relaxation process for carrying out this constraint analysis is described.
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Cites
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Research works printed but not linked: 9
- Attneave F., 1968, Triangles as ambiguous figures, Physiol., 81, 447
- Binford T. O. 1971 Visual perception by computer. Presented to the IEEE Conference on Systems and Control Miami in December 1971.
- Blum H., 1973, Biological shape and visual science, part 1. theor, Biol., 38, 205
- 10.1098/rstb.1976.0090 · DOI · Paper page
- 10.1098/rspb.1977.0080 · DOI
- Minsky M. 1975 A framework for representing knowledge. In The psychology of computer vision (ed. P. H. Winston) pp. 211-277. New York: McGraw-Hill.
- Roberts L. G. 1965 Machine perception of three-dimensional solids. In Optical and electrooptical information processing (ed. J. T. Tippett et al.) pp. 159-197. Cambridge: M.I.T. Press.
- 10.1016/S0010-9452(73)80024-3 · DOI · Paper page
- Winston P. H. 1975 Learning structural descriptions from examples. In The psychology of computer vision (ed. P. H. Winston) pp. 157-209. New York: McGraw-Hill.
Cited by
Recognition-by-components: A theory of human image understanding.
Biederman, I. (1987) · Psychological Review · doi:10.1037/0033-295x.94.2.115 · Cited by 5,621
Priming and Human Memory Systems
Tulving, E., & Schacter, D. L. (1990) · Science · doi:10.1126/science.2296719 · Cited by 2,595
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550
Natural language and natural selection
Pinker, S., & Bloom, P. (1990) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00081061 · Cited by 1,330
Solving the “real” mysteries of visual perception: The world as an outside memory
O'Regan, J. K. (1992) · Canadian Journal of Psychology / Revue canadienne de psychologie · doi:10.1037/h0084327 · Cited by 997
Neural mechanisms for the recognition of biological movements
Giese, M. A., & Poggio, T. (2003) · Nature Reviews Neuroscience · doi:10.1038/nrn1057 · Cited by 955
Organization and functions of cells responsive to faces in the temporal cortex
Perrett, D. I., Hietanen, J. K., Oram, M. W., & Benson, P. J. (1992) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.1992.0003 · Cited by 882
Human motion tracking for rehabilitation-A survey
Zhou, H., & Hu, H. (2008) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.09.001 · Cited by 813
Population coding of shape in area V4
Pasupathy, A., & Connor, C. E. (2002) · Nature Neuroscience · doi:10.1038/972 · Cited by 552
Cleveland, W. S., & McGill, R. (1984) · Journal of the American Statistical Association · doi:10.2307/2288400 · Cited by 1,796
Shape Representation in Area V4: Position-Specific Tuning for Boundary Conformation
Pasupathy, A., & Connor, C. E. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.5.2505 · Cited by 483
3D Menagerie: Modeling the 3D Shape and Pose of Animals
Zuffi, S., Kanazawa, A., Jacobs, D. W., & Black, M. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.586 · Cited by 381
Tsunoda, K., Yamane, Y., Nishizaki, M., & Tanifuji, M. (2001) · Nature Neuroscience · doi:10.1038/90547 · Cited by 359
50 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
Neurophysiology of shape processing
Perrett, D. I., & Oram, M. W. (1993) · Image and Vision Computing · doi:10.1016/0262-8856(93)90011-5 · Cited by 213
Quilty-Dunn, J., Porot, N., & Mandelbaum, E. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x22002849 · Cited by 205
Sehatpour, P., Molholm, S., Javitt, D. C., & Foxe, J. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.07.049 · Cited by 146
Xu, Z., Zhou, Y., Kalogerakis, E., Landreth, C., & Singh, K. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392379 · Cited by 126
“Skeletal structures are fundamental representations in graphics and vision [Dickinson et al. 2009; Marr and Nishihara 1978; Tagliasacchi et al. 2016].”
BackgroundModeling visual problem solving as analogical reasoning.
Lovett, A., & Forbus, K. (2017) · Psychological Review · doi:10.1037/rev0000039 · Cited by 117
Rent3D: Floor-plan priors for monocular layout estimation
Liu, C., Schwing, A. G., Kundu, K., Urtasun, R., & Fidler, S. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298963 · Cited by 104
Open-source tools for behavioral video analysis: Setup, methods, and best practices
Luxem, K., Sun, J. J., Bradley, S. P., Krishnan, K., Yttri, E., Zimmermann, J., Pereira, T. D., & Laubach, M. (2023) · eLife · doi:10.7554/elife.79305 · Cited by 98
Reward guides attention to object categories in real-world scenes.
Hickey, C., Kaiser, D., & Peelen, M. V. (2015) · Journal of Experimental Psychology: General · doi:10.1037/a0038627 · Cited by 91
Analogous intermediate shape coding in vision and touch
Yau, J. M., Pasupathy, A., Fitzgerald, P. J., Hsiao, S. S., & Connor, C. E. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0904186106 · Cited by 77
Hill, H., & Johnston, A. (2007) · Perception · doi:10.1068/p5523 · Cited by 70
Pietraszewski, D. (2022) · Behavioral and Brain Sciences · doi:10.1017/s0140525x21000583 · Cited by 45
Development of visual object recognition
Ayzenberg, V., & Behrmann, M. (2023) · Nature Reviews Psychology · doi:10.1038/s44159-023-00266-w · Cited by 41
RigNet: Neural Rigging for Articulated Characters
Xu, Z., Zhou, Y., Kalogerakis, E., Landreth, C., & Singh, K. (2020) · arXiv · doi:10.48550/arxiv.2005.00559 · Cited by 13
“Skeletal structures are fundamental representations in graphics and vision [Dickinson et al. 2009; Marr and Nishihara 1978; Tagliasacchi et al. 2016].”
BackgroundOne Model to Rig Them All: Diverse Skeleton Rigging with UniRig
Zhang, J.-P., Pu, C.-F., Guo, M.-H., Cao, Y.-P., & Hu, S.-M. (2025) · ACM Transactions on Graphics · doi:10.1145/3730930 · Cited by 12
Metadata
- Authors
- Marr, D., & Nishihara, H. K.
- Venue
- Proceedings of the Royal Society of London. Series B. Biological Sciences
- Publisher
- The Royal Society
- Published
- 1978-02-23
- Volume
- 200
- Issue
- 1140
- Pages
- 269-294
- ISSN
- 0080-4649 (ISSN-L)
- 2053-9193
- OpenAlex
- W2081519360
- Cited by
- 2,158
- References
- 1
- FWCI
- 69.75
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- royalsociety.org/journals/ethics-policies/data-sharing-mining/ (tdm)
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