blob.extract: Extract Blob Region from Image (Matrix)
In imagefx: Extract Features from Images

Description Usage Arguments Details Value Author(s) See Also Examples

Find the predominant blob region in an image using a Laplacian of Gaussian (LoG) blob detection algorithm. Blob points are found using a connected component algorithm (see Details)

1	blob.extract(img, blob.point, win.size, gaus, lap)

`img`	Matrix with numeric values representing pixel color intensities
`blob.point`	x,y locations of a point that is contained within the sought after blob region. Normally the image's max (or min) value location.
`win.size`	Window size used in connected component algorithm (see Details).
`gaus`	Low pass Gaussian mask that has same dimensions as img
`lap`	Optional high pass Laplacian filter of same dimensions as img. Defaults to standard 5-point stencil.

The LoG algorithm first applies a Gaussian then a Laplacian operator to the image. The resulting image is binarized (values either 0 or 1) depending on sign of values in the LoG Image.

The blob x,y locations surrounding the blob.point and are found via a connected component algorithm. This algorithm is designed for speed and may miss x,y locations if the blob is highly irregular or concave. Adjusting the win.size can yield more accurate blob locations but has a slower run time.

List of length 2 where

`xy.coords`	Matrix of x,y locations of blob in image
`bin.image`	Image (matrix) of same dimension of `img` that gives the binarized result from the LoG Blob Detection

Alex J.C. Witsil

build.gaus filt3d

############
### EG 1 ###
############
## example with synthetic data

## create an image that is a simple gaussian
img <- build.gaus(100,100,sig.x=2,sig.y=10,x.mid=80,y.mid=60)

## find the where the maximum value is
img.max <- which(img==max(img),arr.ind=TRUE)

## define a sigma for the low pass filter
sig=5

## define the low pass filter as another gaussian
lp.filt <- build.gaus(nrow(img),ncol(img),sig.x=sig)

## define a window size for the connected component algorithm
win.size=0.05

## perform the blob detection
blob <- blob.extract(img=img, blob.point=img.max,win.size=win.size,gaus=lp.filt)

####################
### PLOTTING EG1 ###
####################

## define x and y grid lines
grid.x <- 1:nrow(img)
grid.y <- 1:ncol(img)

dev.new()
close.screen(all.screens=TRUE)
split.screen(c(1,3))

screen(1)
image(grid.x,grid.y,img,main='Image')

screen(2)
image(grid.x,grid.y,blob$bin.image,col=gray.colors(2),main='Binarized LoG Image')

screen(3)
image(grid.x,grid.y,img,main='Img with Blob Detected')
points(blob$xy.coords,col='black',pch=16,cex=1)

## close the screens
close.screen(all.screens=TRUE)


############
### EG 2 ###
############
## example with volcano image data.
## This RBG image shows ash erupting above the crater, which is cropped out

data(sakurajima)

## crop accroding to these corner values
xleft = 1
xright = 188
ybottom = 1
ytop = 396

## crop the image using crop.image
cropped <- crop.image(sakurajima, xleft, ybottom, xright, ytop)

## redefine the crop image
img <- cropped$img.crop

######################
### PRE PROCESSING ###
######################

## separate the image into red, green, and blue images
r.img <- img[,,1]
g.img <- img[,,2]
b.img <- img[,,3]

## remove the mean
r.img <- r.img-mean(r.img)
g.img <- g.img-mean(g.img)
b.img <- b.img-mean(b.img)

## calculate the the plane trend...
r.img.trend <- fit3d(r.img)
g.img.trend <- fit3d(g.img)
b.img.trend <- fit3d(b.img)

## remove the trend
r.img.dtrend <- r.img-r.img.trend
g.img.dtrend <- g.img-g.img.trend
b.img.dtrend <- b.img-b.img.trend


################################
### SET UP SOME FILTER MASKS ###
################################

## define a sigma for the LP Gaussian Filter
gaus.sig=15

## build the Gaussian filter
gaus <- build.gaus(nrow(img),ncol(img),gaus.sig)

## find the extreme (absolute valued maximum) value of each RGB channel
blob.r.point <- which(abs(r.img.dtrend)==max(abs(r.img.dtrend)),arr.ind=TRUE)
blob.g.point <- which(abs(g.img.dtrend)==max(abs(g.img.dtrend)),arr.ind=TRUE)
blob.b.point <- which(abs(b.img.dtrend)==max(abs(b.img.dtrend)),arr.ind=TRUE)

## set a window size to be used in the connected component algorithm
win.size = 0.05

## extract the blob xy locations
blob.r <- blob.extract(r.img.dtrend,blob.r.point,win.size,gaus)
blob.g <- blob.extract(g.img.dtrend,blob.r.point,win.size,gaus)
blob.b <- blob.extract(b.img.dtrend,blob.r.point,win.size,gaus)


####################
### PLOTTING EG2 ###
####################

## note the blob points (blob$xy.coords) must be adjusted according to
## where the origin (0,0) is located in R plots image plots
blob.coords.r  <- blob.r$xy.coords
blob.coords.r[,1] <- blob.r$xy.coords[,2]
blob.coords.r[,2] <- (blob.r$xy.coords[,1]-nrow(r.img))*-1

blob.coords.g  <- blob.g$xy.coords
blob.coords.g[,1] <- blob.g$xy.coords[,2]
blob.coords.g[,2] <- (blob.g$xy.coords[,1]-nrow(g.img))*-1

blob.coords.b  <- blob.b$xy.coords
blob.coords.b[,1] <- blob.b$xy.coords[,2]
blob.coords.b[,2] <- (blob.b$xy.coords[,1]-nrow(b.img))*-1


## save the users options
mar.usr=par()$mar

dev.new()
close.screen(all.screen=TRUE)
par(mar=c(0,0,2,0))
split.screen(c(1,2))
split.screen(c(3,1),screen=2)

screen(1)
image2(sakurajima,asp=1,axes=FALSE)
rect(ybottom,nrow(sakurajima)-xleft,ytop,nrow(sakurajima)-xright,lwd=3,border='white',lty=3)
title('Original Image',line=0,font=2,col='white',cex=2,)

screen(3)
image2(r.img,asp=1,axes=FALSE)
points(blob.coords.r,col=rgb(1,0,0,alpha=0.05),pch=16,cex=0.3)
title('Red Channel',line=0,font=2,col='red',cex=2)

screen(4)
image2(g.img,asp=1,axes=FALSE)
points(blob.coords.g,col=rgb(0,1,0,alpha=0.05),pch=16,cex=0.3)
title('Green Channel',line=0,font=2,col='darkgreen',cex=2)

screen(5)
image2(b.img,asp=1,axes=FALSE)
points(blob.coords.b,col=rgb(0,0,1,alpha=0.05),pch=16,cex=0.3)
title('Blue Channel',line=0,font=2,col='darkblue',cex=2)

## return the users original margins and close screens
par(mar=mar.usr)
close.screen(all.screens=TRUE)