The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A Robust Infrared Small Target Detection Algorithm Based on Human Visual System
Open accessAbstract
Robust human visual system (HVS) properties can effectively improve the infrared (IR) small target detection capabilities, such as detection rate, false alarm rate, speed, etc. However, current algorithms based on HVS usually improve one or two of the aforementioned detection capabilities while sacrificing the others. In this letter, a robust IR small target detection algorithm based on HVS is proposed to pursue good performance in detection rate, false alarm rate, and speed simultaneously. First, an HVS size-adaptation process is used, and the IR image after preprocessing is divided into subblocks to improve detection speed. Then, based on HVS contrast mechanism, the improved local contrast measure, which can improve detection rate and reduce false alarm rate, is proposed to calculate the saliency map, and a threshold operation along with a rapid traversal mechanism based on HVS attention shift mechanism is used to get the target subblocks quickly. Experimental results show the proposed algorithm has good robustness and efficiency for real IR small target detection applications.
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
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Koch, C., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,271
The relationship between Precision-Recall and ROC curves
Davis, J., & Goadrich, M. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143874 · Cited by 6,559
Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry
Koch, C., & Ullman, S. (1987) · Matters of Intelligence · doi:10.1007/978-94-009-3833-5_5 · Cited by 3,722
A Local Contrast Method for Small Infrared Target Detection
Chen, C. L. P., Li, H., Wei, Y., Xia, T., & Tang, Y. Y. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2242477 · Cited by 1,351
Olshausen, B., Anderson, C., & Van Essen, D. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-11-04700.1993 · Cited by 908
Infrared dim target detection based on visual attention
Wang, X., Lv, G., & Xu, L. (2012) · Infrared Physics & Technology · doi:10.1016/j.infrared.2012.08.004 · Cited by 241
Small Target Detection Utilizing Robust Methods of the Human Visual System for IRST
Kim, S., Yang, Y., Lee, J., & Park, Y. (2009) · Journal of Infrared, Millimeter, and Terahertz Waves · doi:10.1007/s10762-009-9518-2 · Cited by 189
Target detection based on a dynamic subspace
Du, B., & Zhang, L. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2013.07.005 · Cited by 165
Visual saliency and spike timing in the ventral visual pathway
VanRullen, R. (2003) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2003.09.010 · Cited by 109
Object Detection With DoG Scale-Space: A Multiple Kernel Learning Approach
Nilufar, S., Ray, N., & Zhang, H. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2192130 · Cited by 35
Cited by
Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm
Zhang, L., & Peng, Z. (2019) · Remote Sensing · doi:10.3390/rs11040382 · Cited by 759
Bio-Inspired Video Enhancement for Small Moving Target Detection
Uzair, M., Brinkworth, R. S., & Finn, A. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3043113 · Cited by 42
Metadata
- Authors
- Han, J., Ma, Y., Zhou, B., Fan, F., Liang, K., & Fang, Y.
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2014-12
- Volume
- 11
- Issue
- 12
- Pages
- 2168-2172
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2080197848
- Cited by
- 538
- References
- 10
- FWCI
- 24.48
- Type
- journal article
- Language
- en
- Open access
- bronze
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: A Robust Infrared Small Target Detection Algorithm Based on Human Visual System. https://app.cochat.ai/papers/10.1109/lgrs.2014.2323236
- The paper
- Han, J., Ma, Y., Zhou, B., Fan, F., Liang, K., & Fang, Y. (2014). A Robust Infrared Small Target Detection Algorithm Based on Human Visual System. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/lgrs.2014.2323236
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.