The DOI matches its Crossref record.
Computer Matching of Areas in Stereo Images
Abstract
Abstract : This dissertation describes techniques for efficiently matching corresponding areas of a stereo pair of images. Measures of match which are suitable for this purpose are discussed, as are methods for pruning the search for a match. The mathematics necessary to convert a set of matchings into a workable camera model are given, along with calculations which use this model and a pair of image points to locate the corresponding scene point. Methods are included to detect some types of unmatchable target areas in the original data and for detecting when a supposed match is invalid. Region growing techniques are di9 for extend matching areas into regions of constant parallax and for delimiting uniform regions in an image. Also, two algorithms are presented to show some of the ways in which these techniques can be combined to perform useful tasks in the processing of stereo images.
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
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
Non-parametric local transforms for computing visual correspondence
Zabih, R., & Woodfill, J. (1994) · Lecture Notes in Computer Science · doi:10.1007/bfb0028345 · Cited by 1,763
Scaramuzza, D., & Fraundorfer, F. (2011) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2011.943233 · Cited by 1,550
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Scharstein, D., Szeliski, R., & Zabih, R. (2002) · Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001) · doi:10.1109/smbv.2001.988771 · Cited by 1,084
A system for extracting three-dimensional measurements from a stereo pair of TV cameras
Yakimovsky, Y., & Cunningham, R. (1978) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(78)90112-0 · Cited by 306
Metadata
- Authors
- Hannah, M. J.
- Publisher
- Defense Technical Information Center
- Published
- 1974-07-01
- OpenAlex
- W2103584248
- Cited by
- 184
- References
- 0
- Type
- report
- 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: Computer Matching of Areas in Stereo Images. https://app.cochat.ai/papers/10.21236/ad0786720
- The paper
- Hannah, M. J. (1974). Computer Matching of Areas in Stereo Images. https://doi.org/10.21236/ad0786720
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.
