The DOI matches its Crossref record; 31 of the 33 references it cites were found in a registry or an index.
The Nonlinear Statistics of High-Contrast Patches in Natural Images
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The full data set consists of roughly 8,000,000 points in nine-dimensional Euclidean space.
“The full data set consists of roughly 8,000,000 points in E 9 .”
From Barcodes: the persistent topology of data (2007) · cites this paper as [18]
Section 2 (page unavailable) reports about 4.2 million optical patches and 0.79 million range patches—roughly 5 million total, not 8 million. Section 3 (page unavailable) represents each original 3 × 3 patch as a vector in nine-dimensional space, although the preprocessed data lie on a seven-dimensional sphere. (LLM, conf=0.90)
Experimental — accuracy not yet measuredThe database M consists of approximately 4.5×10^6 points on a 7-dimensional ellipsoid in R^8.
“The result of this construction is a database M of ca.4.5 × 10 6 points on a 7-dimensional ellipsoid in R 8 .”
From Topology and data (2009) · cites this paper as [41]
Section 3 (Preprocessing; page unavailable) says the preprocessed points lie on a 7-dimensional ellipsoid in R⁹, which is transformed into a 7-dimensional sphere in R⁸. Section 2 (Optical and Range Data Sets; page unavailable) reports about 4.2 million optical patches, not approximately 4.5 million points in a database M. The source supports the general dimensional construction, but not the stated count and geometry together. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAfter normalizing mean intensity and restricting to high-contrast images, the data set is projected to points on a topological seven-sphere in eight-dimensional Euclidean space.
“By normalizing with respect to mean intensity and restricting attention to high-contrast images (those away from the origin), the data set is projected to a set of points M on a topological seven-sphere S 7 ⊂ E 8 .”
From Barcodes: the persistent topology of data (2007) · cites this paper as [18]
The first two conditions (∫D f=0 and f=q(λ(x))) are analogous to the requirements that the mean intensity be zero and the contrast be one, which were imposed on the data set in Lee et al. (2003).
“The first two conditions are analogous to the requirements that the mean intensity be zero and the contrast be one, which were imposed on the data set in Lee et al. (2003).”
From Topological pattern recognition for point cloud data (2014) · cites this paper as Lee et al. (2003)
In [41], indications were found that the data was concentrated around an annulus.
“In particular, in [41], indications were found that the data was concentrated around an annulus.”
From Topology and data (2009) · cites this paper as [41]
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Metadata
- Authors
- Lee, A. B., Pedersen, K. S., & Mumford, D.
- Affiliations
- Brown University (US)
- University of Copenhagen (DK)
- ORCID
- Pedersen, Kim S.
- Venue
- International Journal of Computer Vision
- Published
- 2003-08
- Volume
- 54
- Issue
- 1-3
- Pages
- 83-103
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2120023453
- Cited by
- 234
- References
- 33
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 5 · 2023: 4 · 2022: 5 · 2021: 11 · 2020: 12 · 2019: 10 · 2018: 9 · 2017: 12 · 2016: 10 · 2015: 9 · 2014: 12 · 2013: 11 · 2012: 18
- FWCI
- 10.70
- Type
- journal article
- Language
- en
- Open access
- green
- nrs.harvard.edu
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Funders
- Army Research Office
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- natural image statistics, density estimation, low-dimensional manifolds, optical imagery, 3D range images, intrinsic dimension
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 ›
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