Statistics of spatial cone-excitation ratios in natural scenes

Nascimento, Sérgio M. C.; Ferreira, Flávio P.; Foster, David H.

doi:10.1364/josaa.19.001484

Cited by 223 publications

(198 citation statements)

References 30 publications

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“…Detailed description of the system and acquisition methodology is given elsewhere (Pinto, Linhares & Nascimento, 2008). The spectral accuracy of the hyperspectral system in recovering spectral reflectance factors of colored samples is within 2% , Nascimento, Ferreira & Foster, 2002. The paintings were simulated illuminated by the standard illuminant D65 and the corresponding coordinates of each pixel in the CIELAB space computed.…”

Section: Stimuli and Experimental Set-upmentioning

confidence: 99%

The colors of paintings and viewers’ preferences

et al. 2017

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Abstract. One hypothesis to explain the aesthetics of paintings is that it depends on the extent to which they mimic natural image statistics. In fact, paintings and natural scenes share several statistical image regularities but the colors of paintings seem generally more biased towards red than natural scenes. Is the particular option for colors in each painting, even if less naturalistic, critical for perceived beauty? Here we show that it is. In the experiments, 50 naïve observers, unfamiliar with the 10 paintings tested, could rotate the color gamut of the paintings and select the one producing the best subjective impression. The distributions of angles obtained are described by normal distributions with maxima deviating, on average, only 7 degrees from the original gamut orientation and full width at half maximum just above the threshold to perceive a chromatic change in the paintings. Crucially, for data pooled across observers and abstract paintings the maximum of the distribution was at zero degrees, i.e., the same as the original. This demonstrates that artists know what chromatic compositions match viewers' preferences and that the option for less naturalistic colors does not constraint the aesthetic value of paintings.3

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Section: Stimuli and Experimental Set-upmentioning

confidence: 99%

The colors of paintings and viewers’ preferences

et al. 2017

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“…The third dataset is of size 820 × 820 × 31 (31 wavelengths). It was obtained online 3 , from a hyperspectral image database at the University of Manchester [50]; these data are henceforth referred to as the Objects data. Sample encoded images of all three datasets, as well as colored (RGB) pictures of the latter two scenes, are shown in Figure 2.…”

Section: E Results On Synthetic Encodings Of Phantoms and Natural Scmentioning

confidence: 99%

Coded Hyperspectral Imaging and Blind Compressive Sensing

Rajwade¹,

Kittle²,

Tsai³

et al. 2013

SIAM J. Imaging Sci.

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Blind compressive sensing (CS) is considered for reconstruction of hyperspectral data imaged by a coded aperture camera. The measurements are manifested as a superposition of the coded wavelengthdependent data, with the ambient three-dimensional hyperspectral datacube mapped to a two-dimensional measurement. The hyperspectral datacube is recovered using a Bayesian implementation of blind CS.Several demonstration experiments are presented, including measurements performed using a coded aperture snapshot spectral imager (CASSI) camera. The proposed approach is capable of efficiently reconstructing large hyperspectral datacubes. Comparisons are made between the proposed algorithm and other techniques employed in compressive sensing, dictionary learning and matrix factorization. Index Termshyperspectral images, image reconstruction, projective transformation, dictionary learning, non-parametric Bayesian, Beta-Bernoulli model, coded aperture snapshot spectral imager (CASSI).

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“…This is also confirmed by the histograms in Figure 19 where we see the histograms of the Red, Green and Blue channels for the original, spectrally computed image (marked by a *), the PCA-based estimate (marked by a hexagram) and the RGB-based estimate (marked by a circle). This is based on the prediction of frame 50 from the data computed from frame 25. We see that the PCA-based estimate is slightly better, especially in the red channel.…”

Section: Applicationsmentioning

confidence: 95%

“…The multispectral image consists of 31 bands described in [25] and the camera calibration procedure can be found in [33].…”

Section: Applicationsmentioning

confidence: 99%

Lie methods for color robot vision

Lenz

2008

Robotica

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We describe how Lie-theoretical methods can be used to analyze color related problems in machine vision. The basic observation is that the non-negative nature of spectral color signals restricts these functions to be members of a limited, conical section of the larger Hilbert space of square-integrable functions. From this observation we conclude that the space of color signals can be equipped with a coordinate system consisting of a half-axis and a unit ball with the Lorentz groups as natural transformation group. We introduce the theory of the Lorentz group SU(1, 1) as a natural tool for analyzing color image processing problems and derive some descriptions and algorithms that are useful in the investigation of dynamical color changes. We illustrate the usage of these results by describing how to use compress, interpolate, extrapolate and compensate image sequences generated by dynamical color changes.

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Statistics of spatial cone-excitation ratios in natural scenes

Cited by 223 publications

References 30 publications

The colors of paintings and viewers’ preferences

The colors of paintings and viewers’ preferences

Coded Hyperspectral Imaging and Blind Compressive Sensing

Lie methods for color robot vision

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