The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Computational visual attention systems and their cognitive foundations: A survey
Abstract
Based on concepts of the human visual system, computational visual attention systems aim to detect regions of interest in images. Psychologists, neurobiologists, and computer scientists have investigated visual attention thoroughly during the last decades and profited considerably from each other. However, the interdisciplinarity of the topic holds not only benefits but also difficulties: Concepts of other fields are usually hard to access due to differences in vocabulary and lack of knowledge of the relevant literature. This article aims to bridge this gap and bring together concepts and ideas from the different research areas. It provides an extensive survey of the grounding psychological and biological research on visual attention as well as the current state of the art of computational systems. Furthermore, it presents a broad range of applications of computational attention systems in fields like computer vision, cognitive systems, and mobile robotics. We conclude with a discussion on the limitations and open questions in the field.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
Posner, M. I. (1980) · Quarterly Journal of Experimental Psychology · doi:10.1080/00335558008248231 · Cited by 9,670 · cited 6×
“The performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at an uncued location; this was called the Posner cueing paradigm [Posner 1980].”
InfluentialNeural 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
The attention system of the human brain
Posner, M. I., & Petersen, S. E. (1990) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.13.030190.000325 · Cited by 8,070
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
Hierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309
Guided search: An alternative to the feature integration model for visual search.
Wolfe, J. M., Cave, K. R., & Franzel, S. L. (1989) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.15.3.419 · Cited by 2,147
Contextual Cueing: Implicit Learning and Memory of Visual Context Guides Spatial Attention
Chun, M. M., & Jiang, Y. (1998) · Cognitive Psychology · doi:10.1006/cogp.1998.0681 · Cited by 1,959
Visual attention within and around the field of focal attention: A zoom lens model
Eriksen, C. W., & St. James, J. D. (1986) · Perception & Psychophysics · doi:10.3758/bf03211502 · Cited by 1,907
Cited by
Attention and choice: A review on eye movements in decision making
Orquin, J. L., & Mueller Loose, S. (2013) · Acta Psychologica · doi:10.1016/j.actpsy.2013.06.003 · Cited by 940
Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022) · Psychology & Marketing · doi:10.1002/mar.21619 · Cited by 631
40 years of cognitive architectures: core cognitive abilities and practical applications
Kotseruba, I., & Tsotsos, J. K. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-018-9646-y · Cited by 539
DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations
Kruthiventi, S. S. S., Ayush, K., & Babu, R. V. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2710620 · Cited by 478
Mechanisms of top-down attention
Baluch, F., & Itti, L. (2011) · Trends in Neurosciences · doi:10.1016/j.tins.2011.02.003 · Cited by 437
Metadata
- Authors
- Frintrop, S., Rome, E., & Christensen, H. I.
- Venue
- ACM Transactions on Applied Perception
- Published
- 2010-01
- Volume
- 7
- Issue
- 1
- Pages
- 1-39
- ISSN
- 1544-3558 (ISSN-L)
- 1544-3965
- OpenAlex
- W2068419217
- Cited by
- 389
- References
- 15
- FWCI
- 41.65
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Computational visual attention systems and their cognitive foundations: A survey. https://app.cochat.ai/papers/10.1145/1658349.1658355
- The paper
- Frintrop, S., Rome, E., & Christensen, H. I. (2010). Computational visual attention systems and their cognitive foundations: A survey. ACM Transactions on Applied Perception. https://doi.org/10.1145/1658349.1658355
Data sources
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.
