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Items: 1 to 20 of 86

1.

A three-layer model of natural image statistics.

Gutmann MU, Hyvärinen A.

J Physiol Paris. 2013 Nov;107(5):369-98. doi: 10.1016/j.jphysparis.2013.01.001. Epub 2013 Jan 29.

PMID:
23369823
2.

Statistical models of natural images and cortical visual representation.

Hyvärinen A.

Top Cogn Sci. 2010 Apr;2(2):251-64. doi: 10.1111/j.1756-8765.2009.01057.x. Epub 2009 Nov 4.

3.

Sparsity-regularized HMAX for visual recognition.

Hu X, Zhang J, Li J, Zhang B.

PLoS One. 2014 Jan 2;9(1):e81813. doi: 10.1371/journal.pone.0081813. eCollection 2014.

4.

Brain responses strongly correlate with Weibull image statistics when processing natural images.

Scholte HS, Ghebreab S, Waldorp L, Smeulders AW, Lamme VA.

J Vis. 2009 Apr 30;9(4):29.1-15. doi: 10.1167/9.4.29.

PMID:
19757938
5.

First- and second-order information in natural images: a filter-based approach to image statistics.

Johnson AP, Baker CL Jr.

J Opt Soc Am A Opt Image Sci Vis. 2004 Jun;21(6):913-25.

PMID:
15191171
6.

Invariant visual object recognition: a model, with lighting invariance.

Rolls ET, Stringer SM.

J Physiol Paris. 2006 Jul-Sep;100(1-3):43-62. Epub 2006 Oct 30. Review.

PMID:
17071062
7.

Nonlinear and higher-order approaches to the encoding of natural scenes.

Zetzsche C, Nuding U.

Network. 2005 Jun-Sep;16(2-3):191-221.

PMID:
16411496
8.

Efficient coding of natural images.

Ma LB, Wu S.

Sheng Li Xue Bao. 2011 Oct 25;63(5):463-71. Review.

9.

Learning intermediate-level representations of form and motion from natural movies.

Cadieu CF, Olshausen BA.

Neural Comput. 2012 Apr;24(4):827-66. doi: 10.1162/NECO_a_00247. Epub 2011 Dec 14.

PMID:
22168556
11.

Power spectra of the natural input to the visual system.

Pamplona D, Triesch J, Rothkopf CA.

Vision Res. 2013 May 3;83:66-75. doi: 10.1016/j.visres.2013.01.011. Epub 2013 Mar 1.

12.

Image/source statistics of surfaces in natural scenes.

Yang Z, Purves D.

Network. 2003 Aug;14(3):371-90.

PMID:
12938763
13.

Invariant texture perception is harder with synthetic textures: Implications for models of texture processing.

Balas B, Conlin C.

Vision Res. 2015 Oct;115(Pt B):271-9. doi: 10.1016/j.visres.2015.01.022. Epub 2015 Feb 7.

14.

Receptive field self-organization in a model of the fine structure in v1 cortical columns.

Lücke J.

Neural Comput. 2009 Oct;21(10):2805-45. doi: 10.1162/neco.2009.07-07-584.

PMID:
19548804
15.

Learning optimized features for hierarchical models of invariant object recognition.

Wersing H, Körner E.

Neural Comput. 2003 Jul;15(7):1559-88.

PMID:
12816566
16.

Learning invariance from natural images inspired by observations in the primary visual cortex.

Teichmann M, Wiltschut J, Hamker F.

Neural Comput. 2012 May;24(5):1271-96. doi: 10.1162/NECO_a_00268. Epub 2012 Feb 1.

PMID:
22295987
17.
18.

Spatial scene representations formed by self-organizing learning in a hippocampal extension of the ventral visual system.

Rolls ET, Tromans JM, Stringer SM.

Eur J Neurosci. 2008 Nov;28(10):2116-27. doi: 10.1111/j.1460-9568.2008.06486.x.

PMID:
19046392
19.

Nonmonotonic noise tuning of BOLD fMRI signal to natural images in the visual cortex of the anesthetized monkey.

Rainer G, Augath M, Trinath T, Logothetis NK.

Curr Biol. 2001 Jun 5;11(11):846-54.

20.

Predicting the human reaction time based on natural image statistics in a rapid categorization task.

Mirzaei A, Khaligh-Razavi SM, Ghodrati M, Zabbah S, Ebrahimpour R.

Vision Res. 2013 Apr 5;81:36-44. doi: 10.1016/j.visres.2013.02.003. Epub 2013 Feb 16.

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