RNLachrom: Weber achromatic contrast for the Receptor Noise Limited...

In colourvision: Colour Vision Models

Weber achromatic contrast for the Receptor Noise Limited Model (Vorobyev & Osorio 1998)

Description

Weber achromatic contrast for the Receptor noise limited model (Vorobyev & Osorio 1998; Vorobyev et al. 1998).

Usage

RNLachrom(R1, R2=Rb, Rb, I, C, e,
         interpolate = TRUE, nm = seq(300, 700, 1))

Arguments

R1	Reflectance of observed objects. A data frame with first column corresponding to wavelength values and following columns with reflectance values. R1 must be in the same scale as R2 and Rb (percentage or proportion).
R2	Reflectance to be compared against R1. R2=Rb if comparison is to be made against the background. A data frame with two columns only: first column corresponding to wavelength values and second column with reflectance values. R2 must be in the same scale as R1 and Rb (percentage or proportion).
Rb	Background reflectance. A data frame with two columns only: first column corresponding to wavelength values and second column with reflectance values. Rb must be in the same scale as R1 and R2 (percentage or proportion).
I	Irradiance spectrum. A data frame with two columns only: first column corresponding to wavelength values and second column with irradiance values. Irradiance values must be in quantum flux units.
C	Photoreceptor sensitivity curves, from lowest to longest lambda-max. A data frame: first column corresponding to wavelength values and following columns with photoreceptor sensitivity values (see function photor).
e	Noise of the photoreceptor.
interpolate	Whether data files should be interpolated before further calculations. See approx.
nm	A sequence of numeric values specifying where interpolation is to take place. See approx.

Details

The Weber achromatic contrast for a single photoreceptor is calculated by:

\Delta S = |\frac{\ln(Qr_1)-\ln(Qr_2)}{e}|

where Qr_1 and Qr_2 are the relative photoreceptor quantum catches from stimulus 1 (R1) and stimulus 2 (R2).

Noise may be dependent of the intensity, but this possibility is not implement in colourvision yet. Noise dependent of intensity usually holds for low light conditions only (Vorobyev et al. 1998).

Value

e1	Photoreceptor noise.
Qr1_R1	Photoreceptor photon catch value from R1, after the von Kries transformation (see function Qr).
Qr1_R2	Photoreceptor photon catch value from R2, after the von Kries transformation (see function Qr).
E1_R1	Photoreceptor output from the stimulus (R1)
E1_R2	Photoreceptor output from R2
deltaS	Euclidean distance from R1 to R2. It represents the achromatic contrast of the stimulus (R1) in relation to the background when R1=Rb.

Author(s)

Felipe M. Gawryszewski [e-mail]

References

Vorobyev, M., and D. Osorio. 1998. Receptor noise as a determinant of colour thresholds. Proceedings of the Royal Society B 265:351-358.

Vorobyev, M., D. Osorio, A. T. D. Bennett, N. J. Marshall, and I. C. Cuthill. 1998. Tetrachromacy, oil droplets and bird plumage colours. J Comp Physiol A 183:621-633.

See Also

RNLmodel, photor, RNLthres, CTTKmodel, EMmodel, GENmodel

Examples

#1
## Photoreceptor sensitivity spectra
##with lambda max at 350nm, 450nm and 550nm:
C<-photor(lambda.max=c(350))

##Grey background
##with 7 percent reflectance from 300 to 700nm:
Rb <- data.frame(300:700, rep(7, length(300:700)))

## Read CIE D65 standard illuminant:
data("D65")

##Reflectance data of R1 and R2
R1.1<-logistic(x=seq(300,700,1), x0=500, L=50, k=0.04)
R1.2<-logistic(x=seq(300,700,1), x0=400, L=50, k=0.04)
w<-R1.1[,1]
R1.1<-R1.1[,2]+10
R1.2<-R1.2[,2]+10
R1<-data.frame(w=w, R1.1=R1.1, R1.2=R1.2)

R2<-logistic(x=seq(300,700,1), x0=550, L=50, k=0.04)
R2[,2]<-R2[,2]+10

plot(R1[,c(1,2)],type="l",
     ylim=c(0,60))
lines(R1[,c(1,3)])
lines(R2[,c(1,2)],col="red")
lines(Rb,col="green")

## Run model 
RNLachrom(R1=R1, R2=R2, Rb=Rb, I=D65, C=C,
          e = 0.16)

colourvision documentation built on April 13, 2025, 5:11 p.m.