Measurement and Analysis of Object Reflectance Spectra

Vrhel, M. J.; Gershon, Ron; Iwan, L.

doi:10.1111/j.1520-6378.1994.tb00053.x

Cited by 295 publications

(192 citation statements)

References 23 publications

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“…Some representations of its spectra have been shown to include natural spectra, such as those from flowers, flower clusters, leaves, and berries (Jaaskelainen et al 1990). Previous studies of the Munsell set have found that the number of basis functions needed to approximate its spectra ranges from 3 to 8, depending on the criterion of fit and whether part or all of the set was used (Cohen 1964;Maloney 1986;Parkkinen et al 1989;Jaaskelainen et al 1990;Usui et al 1992;Vrhel et al 1994;Lenz et al 1996;Owens et al 2000;Westland et al 2000;Romney and Indow 2003). In all these studies, however, the adequacy of the approximation was based on theoretical criteria, rather than on psychophysical measurement.…”

Section: Introductionmentioning

confidence: 99%

Perceptual Limits on Low-Dimensional Models of Munsell Reflectance Spectra

Oxtoby

Foster

2005

Perception

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Abstract. Some experimental and theoretical approaches to surface-colour perception depend on approximating surface reflectance spectra by low-dimensional models. In the psychophysical experiment reported here, observers had to discriminate between patterns of Munsell surfaces and their spectral approximations under either the same or different illuminants. The approximations were produced by principal component analysis, by independent component analysis, and by artificial neural networks trained with a supervised-learning rule. In all experimental conditions, observers required, on average, at least 5 basis functions for discrimination performance to be at chance, thus placing a lower limit on the dimensionality of models of Munsell reflectance spectra.

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Section: Introductionmentioning

confidence: 99%

Perceptual Limits on Low-Dimensional Models of Munsell Reflectance Spectra

Oxtoby

Foster

2005

Perception

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“…in the photopic range are closely approximated by three basis functions. They reported the basis functions for both the Munsell color samples and the 708 natural objects reported by Vrhel et al (12) as being virtually identical.…”

Section: A Model Of How Cone Sensitivity Curves Are Aggregated In Thementioning

confidence: 80%

Modeling lateral geniculate nucleus cell response spectra and Munsell reflectance spectra with cone sensitivity curves

Romney

D’Andrade²

2005

Proc. Natl. Acad. Sci. U.S.A.

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We find that the cell response spectra of lateral geniculate nucleus cells, as well as the reflectance spectra of Munsell color chips, may be modeled by using the cone sensitivity functions of the long and medium cones. We propose a simple model for how the neural signals from the photoreceptors might be combined in the retina to closely approximate the reflectance spectra of Munsell color chips without input from the short cone.vision ͉ color perception R ecent research reveals that lateral geniculate nucleus (LGN) cell response spectra are fairly smoothly distributed in Munsell perceptual color space in a manner similar to the reflectance spectra of Munsell color chips (1). The findings were based on data reported by De Valois et al. (2), who measured spectral response data on 147 LGN cells. Neural signals reach the LGN through the optic nerve, which consists of the axons of ganglion cells in the retina. Each axon carries signals aggregated from a number of photoreceptors. The aim of this paper is to examine the LGN response spectra for clues as to how the responses of the long (L), medium (M), and short (S) cones (photoreceptors) are aggregated through the various layers of the retina to produce the LGN response spectra. From these clues, we construct a simple model of how the reflectance spectra of the Munsell chips might be represented by these aggregation processes.We assume that the color appearance of objects originates in their reflectance spectra that, together with the illumination source, determine the light stimuli reaching the photoreceptors or cones in the retina. We seek understanding of how photoreceptors are combined in the retina to produce a distribution of the LGN response spectra that is similar in distribution to the reflectance spectra of objects (1). First, we explore the contribution of the cones to the basis functions of the LGN spectral response curves. We recognize that these representations are statistical constructions and do not necessarily represent how the neural system actually functions. However, we can obtain some insight into the way the system might function from such an examination. We then propose a model of how the cone sensitivity curves might be combined to produce response spectra that closely approximate reflectance spectra of ordinary objects such as flowers, fruits, and Munsell color chips. We denote the data consisting of the response spectra of the 147 LGN cells as R NϫM , where N ϭ 147 cells and M ϭ 12 sampled wavelength locations with responses measured in spikes per second (each cell was measured at three light intensities, and the 147 values are means of the three measures). We used the raw recorded data uncorrected for base firing rates. In general, PCA or singular value decomposition analyzes a matrix such as R in the following way (5) Cone Contributions to the Basis Functions of the LGN Neuronswhere both UU T and VV T are identity matrices, and ⌬ KϫK ϭ ( ͌ k ) is a diagonal matrix. Values of k (Ͼ0) and v jk are obtained, respectively, as eigenvalues and ei...

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“…All errors were computed for simulated scanning of a 64-data-point subset of the set of Munsell color chips [8] followed by data correction [5]. The white point used for the data set was the white sample of the Munsell set.…”

Section: Resultsmentioning

confidence: 99%