The DOI matches its Crossref record.
Registration of Translated and Rotated Images Using Finite Fourier Transforms
Abstract
A well-known method for image registration is based on a conventional correlation between phase-only, or whitened, versions of the two images to be realigned. The method, covering rigid translational movements, is characterized by an outstanding robustness against correlated noise and disturbances, such as those encountered with nonuniform, time varying illumination. This correspondence discusses an extension of the method to cover both translational and rotational movements.
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
Cited by
Image registration methods: a survey
Zitová, B., & Flusser, J. (2003) · Image and Vision Computing · doi:10.1016/s0262-8856(03)00137-9 · Cited by 6,423
Least-Squares Fitting of Two 3-D Point Sets
Arun, K. S., Huang, T. S., & Blostein, S. D. (1987) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1987.4767965 · Cited by 3,952
Mapping the stereotyped behaviour of freely moving fruit flies
Berman, G. J., Choi, D. M., Bialek, W., & Shaevitz, J. W. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0672 · Cited by 565
Tanaka, Y. H., Tanaka, Y. R., Kondo, M., Terada, S.-I., Kawaguchi, Y., & Matsuzaki, M. (2018) · Neuron · doi:10.1016/j.neuron.2018.08.016 · Cited by 77
A Deep Learning Semantic Template Matching Framework for Remote Sensing Image Registration
Li, L., Han, L., Ding, M., Cao, H., & Hu, H. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.09.012 · Cited by 67
Metadata
- Authors
- De Castro, E., & Morandi, C.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1987-09
- Volume
- PAMI-9
- Issue
- 5
- Pages
- 700-703
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W1963943638
- Cited by
- 601
- References
- 0
- FWCI
- 3.23
- 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: Registration of Translated and Rotated Images Using Finite Fourier Transforms. https://app.cochat.ai/papers/10.1109/tpami.1987.4767966
- The paper
- De Castro, E., & Morandi, C. (1987). Registration of Translated and Rotated Images Using Finite Fourier Transforms. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.1987.4767966
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.
