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Cited by 229Image Retrieval and Classification TechniquesVisual perception and processing mechanismsImage and Signal Denoising Methods

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Occlusion Models for Natural Images: A Statistical Study of a Scale-Invariant Dead Leaves Model

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Lee, Ann B., Mumford, David, Huang, Jinggang·2001·International Journal of Computer Vision
DOI 10.1023/a:1011109015675
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Overview

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Crossref · 10.1023/a:1011109015675

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Dead leaf images consist of layered occluding circles drawn from a power law distribution over scales.

    “Dead leaf images (Jeulin, 1997; Lee et al., 2001) consist of layered occluding circles, drawn from a power law distribution over scales.”

    From Deep Unsupervised Learning using Nonequilibrium Thermodynamics (2015) · cites this paper as Lee et al., 2001

  • Dead leaf images have an analytically tractable structure but capture many of the statistical complexities of natural images.

    “They have an analytically tractable structure, but capture many of the statistical complexities of natural images, and therefore provide a compelling test case for natural image models.”

    From Deep Unsupervised Learning using Nonequilibrium Thermodynamics (2015) · cites this paper as Lee et al., 2001

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 31

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  2. Buccigrossi, R.W. and Simoncelli, E.P. 1999. Image compression via joint statistical characterization in the wavelet domain. Proc. IEEE Trans. on Image Processing, 8(12):1688-1701. · DOI
  3. Field, D.J. 1987. Relations between the statistics of natural images and the response properties of cortical cells.J. Optical Society of America, A4:2379-2394. · DOI · Paper page
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  6. Hallinan P.W., Gordon G.G., Yuille, A.L., Giblin, P., and Mumford, D. 1999. Two-and three-dimensional patterns of the face. A.K. Peters Ltd, Natick, MA. · DOI
  7. Heeger, D.J. and Bergen, J.R. 1995. Pyramid based texture analysis/ synthesis. In Computer Graphics Proc., pp. 229-238. · DOI
  8. Huang, J., Lee, A., and Mumford, D. 2000. Statistics of range images. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 1, Hilton Head Island, SC, pp. 324-331.
  9. Huang, J. and Mumford, D. 1999. Statistics of natural images and models. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Vol. 1, Fort Collins, CO, pp. 541-547.
  10. Kendall, W.S. and ThÔnnes, E. 1998. Perfect Simulation in Stochastic Geometry. Preprint 323, Department of Statistics, University of Warwick, UK.
  11. Kingman, J.F.C. 1993. Poisson Processes. Oxford Studies in Probability. Clarendon Press, Oxford.
  12. Lee, A.B. and Mumford, D. 1999. An occlusion model generating scale-invariant images. In Proc. IEEEWorkshop on Statistical and Computational Theories of Vision, Fort Collins, CO.
  13. Malik, J., Belongie, S., Shi, J., and Leung, T. 1999. Textons, contours and regions: Cue integration in image segmentation. In Proc. IEEE Int. Conf. Computer Vision, Corfu, Greece. · DOI
  14. Mallat, S.G. 1989. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. PAMI, 11:674-693. · DOI
  15. Matheron, G. 1975. Random Sets and Integral Geometry. JohnWiley and Sons: New York. Occlusion Models for Natural Images 59
  16. Moulin, P. and Liu, J. 1999. Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors. IEEE Trans. on Information Theory, 453:909-919. · DOI
  17. Mumford, D. and Gidas, B. 2000. Stochastic models for generic images. Quarterly Journal of Applied Mathematics Volume LIX, Number 1, March 2001, pp. 85-111.
  18. Olshausen, B.A. and Field, D.J. 1996. Natural image statistics and efficient coding. Network, 7:333-339. · DOI
  19. Ruderman, D.L. 1994. The statistics of natural images. Network, 5(4):517-548. · DOI
  20. Ruderman, D.L. 1997. Origins of scaling in natural images. Vision Research, 37(23):3385-3395. · DOI
  21. Ruderman, D.L. and Bialek, W. 1994. Statistics of natural images: scaling in the woods. Physical Review Letters, 73(6):814-817. · DOI
  22. Serra, J.P. 1982. Image Analysis and Mathematical Morphology. Academic Press: London.
  23. Simoncelli, E.P. 1999. Modeling the joint statistics of images in the wavelet domain. In Proc. SPIE 44th Annual Meeting, Vol. 3813, Denver, Colorado. · DOI
  24. Simoncelli, E.P. and Adelson, E.H. 1996. Noise removal via bayesianwavelet coring. In Third Int'l Conf. on Image Processing, Lausanne, Switzerland, pp. 379-383. · DOI
  25. Sullivan, J., Blake, A., Isard, M., and MacCormick, J. 1999. Object localization by Bayesian correlation. In Proc. Int. Conf. Computer Vision, pp. 1068-1075. · DOI
  26. van Hateren, J.H. and van der Schaaf, A. 1998. Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. R. Soc. Lond., B265:359-366. · DOI · Paper page
  27. Wainwright, M.J. and Simoncelli, E.P. 2000. Scale mixtures of gaussians and the statistics of natural images. In Advances in Neural Information Processing Systems 12, Solla, Leen, and Müller(Eds.), MIT Press: Cambridge, MA, pp. 885-861.
  28. Zetzsche, B., Wegmann, B., and Barth, E. 1993. Nonlinear aspects of primary vision: Entropy reduction beyond decorrelation. In Int'l Symp. Soc. for Info. Display, Vol. 24, pp. 933-936.
  29. Zhu, S.C. and Guo, C. 2000. Mathematical modeling of clutter: Descriptive vs. generative models. In Proc. of the SPIE AeroSense Conf. on Automatic Target Recognition, Orlando, FL. · DOI
  30. Zhu, S. and Mumford, D. 1997. Prior learning and Gibbs reactiondiffusion. IEEE Trans PAMI, 19(11):1236-1250. · DOI
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Metadata

Authors
Lee, A. B., Mumford, D., & Huang, J.
Affiliations
Brown University (US)
Corresponding author
Lee, Ann B.
Venue
International Journal of Computer Vision
Publisher
Springer Science and Business Media LLC
Published
2001-01
Volume
41
Issue
1-2
Pages
35-59
ISSN
  • 0920-5691 (ISSN-L)
  • 1573-1405
DOI
10.1023/a:1011109015675
OpenAlex
W1829102423
Cited by
229
References
1
Citations by year
2026: 8 · 2025: 11 · 2024: 4 · 2023: 13 · 2022: 6 · 2021: 10 · 2020: 6 · 2019: 4 · 2018: 14 · 2017: 11 · 2016: 6 · 2015: 9 · 2014: 10 · 2013: 7 · 2012: 12
FWCI
9.70
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Computer Vision (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
Keywords
dead leaves model, scale invariance, natural image statistics, image formation model

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