Description Usage Arguments Details Value Note References Examples
Draw a gray-scale Gabor Patch
1 2 3 4 5 6 7 8 9 10 11 | gaborPatch(sf,
theta = 0,
rad = (theta * pi)/180,
pc = 1,
sigma = 1/6,
psi = 0,
grating = c("cosine", "sine"),
npoints = 100,
trim = 0,
trim.col = .5,
...)
|
sf |
number of cycles per image. |
theta |
orientation in degree. See ‘Details’ |
rad |
orientation in radian |
pc |
a fraction (0 to 1) specifying the peak contrast of the Gabor |
sigma |
the standard deviation of the Gaussian mask. Either a single numeric or a numeric vector of length 2. |
psi |
phase offset in radian |
grating |
type of grating to be used. Default to ‘cosine’. |
npoints |
number of points per line used to draw the patch. |
trim |
Gaussian values smaller than the specified value should be trimmed. |
trim.col |
gray level of the trimmed part of the image, between 0 (‘black’) and 1 (‘white’). Default to .5 (‘gray’) Setting it to any other value or |
... |
additional parameters for |
The arguments theta
and rad
is the same thing but in different units. If both are supplied, rad
takes the precedence.
invisibly returns the matrix of the plotted values.
This function is written just for fun; it is not optimized for speed or for performance.
Fredericksen, R. E., Bex, P. J., & Verstraten, F. A. J. (1997). How Big is a Gabor and Why Should We Care? Journal of the Optical Society of America A. 14, 1–12.
Gabor Filter. (2010, June 5). In Wikipedia, the free encyclopedia. Retrieved July 7, 2010, from http://en.wikipedia.org/wiki/Gabor_filter
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | # An imitation of Fredericksen et al.'s (1997) Fig 1.
# that demonstrate the relation between peak contrast
# and perceived size of the Gabor
op <- par(mfcol = c(3, 3), pty = "m", mai = c(0,0,0,0))
for(i in c(.85, .21, .06)){
for(j in c(1/6, 1/7, 1/8)){
gaborPatch(20, pc = i, sigma = j)
}
}
par(op)
## Not run:
# a typical plot of the stimuli and category structure
# often seen in artificial category-learning literatures.
m <- list(c(268, 157), c(332, 93))
covs <- matrix(c(4538, 4351, 4351, 4538), ncol = 2)
II <- grtrnorm(n = 40, np = 2, means = m, covs = covs,
clip.sd = 4, seed = 1234)
II$sf <- .25+(II$x1/50)
II$theta <- II$x2*(18/50)
plot(II[,2:3], xlim = c(-100,600), ylim = c(-200,500),
pch = 21, bg = c("white","gray")[II$category])
abline(a = -175, b = 1)
library(Hmisc)
idx <- c(20, 31, 35, 49, 62)
xpos <- c(0, 100, 300, 350, 550)
ypos <- c(50, 300, 420, -120, 50)
for(i in 1:5)
{
j = idx[i]
segments(x0=II[j,"x1"], y0=II[j,"x2"], x1=xpos[i], y1=ypos[i])
subplot(gaborPatch(sf=II[j,"sf"], theta=II[j,"theta"]), x=xpos[i], y=ypos[i])
}
## End(Not run)
|
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