Literature DB >> 12169429

Color opponency is an efficient representation of spectral properties in natural scenes.

Te-Won Lee1, Thomas Wachtler, Terrence J Sejnowski.   

Abstract

The human visual system encodes the chromatic signals conveyed by the three types of retinal cone photoreceptors in an opponent fashion. This opponency is thought to reduce redundant information by decorrelating the photoreceptor signals. Correlations in the receptor signals are caused by the substantial overlap of the spectral sensitivities of the receptors, but it is not clear to what extent the properties of natural spectra contribute to the correlations. To investigate the influences of natural spectra and photoreceptor spectral sensitivities, we attempted to find linear codes with minimal redundancy for trichromatic images assuming human cone spectral sensitivities, or hypothetical non-overlapping cone sensitivities, respectively. The resulting properties of basis functions are similar in both cases. They are non-orthogonal, show strong opponency along an achromatic direction (luminance edges) and along chromatic directions, and they achieve a highly efficient encoding of natural chromatic signals. Thus, color opponency arises for the encoding of human cone signals, i.e. with strongly overlapping spectral sensitivities, but also under the assumption of non-overlapping spectral sensitivities. Our results suggest that color opponency may in part be a result of the properties of natural spectra and not solely a consequence of the cone spectral sensitivities.

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Mesh:

Year:  2002        PMID: 12169429      PMCID: PMC2940112          DOI: 10.1016/s0042-6989(02)00122-0

Source DB:  PubMed          Journal:  Vision Res        ISSN: 0042-6989            Impact factor:   1.886


  30 in total

1.  Chromatic structure of natural scenes.

Authors:  T Wachtler; T W Lee; T J Sejnowski
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2001-01       Impact factor: 2.129

2.  The spatial transformation of color in the primary visual cortex of the macaque monkey.

Authors:  E N Johnson; M J Hawken; R Shapley
Journal:  Nat Neurosci       Date:  2001-04       Impact factor: 24.884

3.  Neural selectivity for hue and saturation of colour in the primary visual cortex of the monkey.

Authors:  A Hanazawa; H Komatsu; I Murakami
Journal:  Eur J Neurosci       Date:  2000-05       Impact factor: 3.386

4.  Some transformations of color information from lateral geniculate nucleus to striate cortex.

Authors:  R L De Valois; N P Cottaris; S D Elfar; L E Mahon; J A Wilson
Journal:  Proc Natl Acad Sci U S A       Date:  2000-04-25       Impact factor: 11.205

5.  Learning overcomplete representations.

Authors:  M S Lewicki; T J Sejnowski
Journal:  Neural Comput       Date:  2000-02       Impact factor: 2.026

6.  Color-opponent receptive fields derived from independent component analysis of natural images.

Authors:  D R Tailor; L H Finkel; G Buchsbaum
Journal:  Vision Res       Date:  2000       Impact factor: 1.886

7.  Independent component analysis applied to feature extraction from colour and stereo images.

Authors:  P O Hoyer; A Hyvärinen
Journal:  Network       Date:  2000-08       Impact factor: 1.273

8.  High-order contrasts for independent component analysis.

Authors:  J F Cardoso
Journal:  Neural Comput       Date:  1999-01-01       Impact factor: 2.026

9.  Color and luminance information in natural scenes.

Authors:  C A Párraga; G Brelstaff; T Troscianko; I R Moorehead
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  1998-03       Impact factor: 2.129

10.  Independent component filters of natural images compared with simple cells in primary visual cortex.

Authors:  J H van Hateren; A van der Schaaf
Journal:  Proc Biol Sci       Date:  1998-03-07       Impact factor: 5.349

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  18 in total

1.  Trichromatic reconstruction from the interleaved cone mosaic: Bayesian model and the color appearance of small spots.

Authors:  David H Brainard; David R Williams; Heidi Hofer
Journal:  J Vis       Date:  2008-05-29       Impact factor: 2.240

Review 2.  Diverse Cell Types, Circuits, and Mechanisms for Color Vision in the Vertebrate Retina.

Authors:  Wallace B Thoreson; Dennis M Dacey
Journal:  Physiol Rev       Date:  2019-07-01       Impact factor: 37.312

Review 3.  Chromatic clocks: Color opponency in non-image-forming visual function.

Authors:  Manuel Spitschan; Robert J Lucas; Timothy M Brown
Journal:  Neurosci Biobehav Rev       Date:  2017-04-23       Impact factor: 8.989

4.  Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features.

Authors:  Michael D Abràmoff; Wallace L M Alward; Emily C Greenlee; Lesya Shuba; Chan Y Kim; John H Fingert; Young H Kwon
Journal:  Invest Ophthalmol Vis Sci       Date:  2007-04       Impact factor: 4.799

Review 5.  Probing the functions of contextual modulation by adapting images rather than observers.

Authors:  Michael A Webster
Journal:  Vision Res       Date:  2014-10-02       Impact factor: 1.886

6.  Cone selectivity derived from the responses of the retinal cone mosaic to natural scenes.

Authors:  Thomas Wachtler; Eizaburo Doi; Te- Won Lee; Terrence J Sejnowski
Journal:  J Vis       Date:  2007-06-18       Impact factor: 2.240

7.  Natural image coding in V1: how much use is orientation selectivity?

Authors:  Jan Eichhorn; Fabian Sinz; Matthias Bethge
Journal:  PLoS Comput Biol       Date:  2009-04-03       Impact factor: 4.475

8.  Representation of color stimuli in awake macaque primary visual cortex.

Authors:  Thomas Wachtler; Terrence J Sejnowski; Thomas D Albright
Journal:  Neuron       Date:  2003-02-20       Impact factor: 17.173

9.  Spatiochromatic receptive field properties derived from information-theoretic analyses of cone mosaic responses to natural scenes.

Authors:  Eizaburo Doi; Toshio Inui; Te-Won Lee; Thomas Wachtler; Terrence J Sejnowski
Journal:  Neural Comput       Date:  2003-02       Impact factor: 2.026

10.  Spatiotemporal dynamics of the functional architecture for gain fields in inferior parietal lobule of behaving monkey.

Authors:  Ralph M Siegel; Jeng-Ren Duann; Tzyy-Ping Jung; Terrence Sejnowski
Journal:  Cereb Cortex       Date:  2006-04-07       Impact factor: 5.357

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