Object Categorization: Computer and Human Vision Perspectives
Abstract
This edited volume presents a unique multidisciplinary perspective on the problem of visual object categorization. The result of a series of four highly successful workshops on the topic, the book gathers many of the most distinguished researchers from both computer and human vision to reflect on their experience, identify open problems, and foster a cross-disciplinary discussion with the idea that parallel problems and solutions have arisen in both domains. Twenty-seven of these workshop speakers have contributed chapters, including fourteen from computer vision and thirteen from human vision. Their contributions range from broad perspectives on the problem to more specific approaches, collectively providing important historical context, identifying the major challenges, and presenting recent research results. This multidisciplinary collection is the first of its kind on the topic of object categorization, providing an outstanding context for graduate students and researchers in both computer and human vision.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Good feature representations are of primary importance in object detection.
“As one of the main components in any detector, good feature representations are of primary importance in object detection (Dickinson et al. 2009;Girshick et al. 2014;Gidaris and Komodakis 2015;Zhu et al. 2016a).”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Dickinson et al. 2009
Cited by
Sorted by citations
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965 · see citing works
The Interface Theory of Perception
Hoffman, D. D., Singh, M., & Prakash, C. (2015) · Psychonomic Bulletin & Review · doi:10.3758/s13423-015-0890-8 · Cited by 309 · see citing works
Natural selection and veridical perceptions
Mark, J. T., Marion, B. B., & Hoffman, D. D. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.07.020 · Cited by 97 · see citing works
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 · see citing works
Natural Selection and Shape Perception
Singh, M., & Hoffman, D. D. (2013) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-1-4471-5195-1_12 · Cited by 23 · see citing works
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 · see citing works
“Skeletal structures are fundamental representations in graphics and vision [Dickinson et al. 2009; Marr and Nishihara 1978; Tagliasacchi et al. 2016].”
Background
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2009-09-07
- OpenAlex
- W1553839376
- Cited by
- 110
- References
- 0
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 9 · 2020: 7 · 2019: 4 · 2018: 3 · 2017: 4 · 2016: 4 · 2015: 7 · 2014: 11 · 2013: 13 · 2012: 11
- FWCI
- 5.45
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Keywords
- object classification, visual object recognition, computer vision, human vision, interdisciplinary research
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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