IMAGing engINE, Tools for application of image filters to data matrices

Description

Provides fast application of image filters to data matrices, using R and C++ algorithms.

Details

This package uses C++ algorithms called 'engines'. More details are shown in the vignette.

Author(s)

Maintainer: Wencheng Lau-Medrano luis.laum@gmail.com

See Also

Useful links:

• https://github.com/LuisLauM/imagine

• Report bugs at https://github.com/LuisLauM/imagine/issues

Performs algorithms from Agenbag et al. (2003)

Description

This function performs two (gradient) calculation approaches for SST, as outlined in the paper by Agenbag et al. (2003).

Usage

agenbagFilters(X, algorithm = c(1, 2), ...)

Arguments

Details

Section 2.2.4 of the paper by Agenbag et al. (2003) introduces the following two methods:

Method 1:

Based on the equation

Y_{i,j}=\sqrt{(X_{i+1,j}-X_{i-1,j})^2 +(X_{i,j+1}-X_{i,j-1})^2}

where Y_{i,j} represents the output value for each X_{i,j} pixel value of a given X matrix.

Method 2:

the standard deviation in a 3x3 pixel area centered on position (i,j).

As outlined in the original study, this method conducts searches within a 1-pixel vicinity of each point. For method 1, it only returns a value for points where none of the four involved values are NA. Conversely, for method 2, the standard deviation calculation is performed only for points where at least 3 non-NA values are found in the 3x3 neighborhood.

Value

agenbagFilters returns a matrix object with the same dimensions of X.

References

Agenbag, J.J., A.J. Richardson, H. Demarcq, P. Freon, S. Weeks, and F.A. Shillington. "Estimating Environmental Preferences of South African Pelagic Fish Species Using Catch Size- and Remote Sensing Data". Progress in Oceanography 59, No 2-3 (October 2003): 275-300. (doi:10.1016/j.pocean.2003.07.004).

Examples

data(wbImage)

# Agenbag, method 1
agenbag1 <- agenbagFilters(X = wbImage, algorithm = 1)

# Agenbag, method 2
agenbag2 <- agenbagFilters(X = wbImage, algorithm = 2)

# Plotting results
par(mfrow = c(3, 1), mar = rep(0, 4))

# Original
image(wbImage, axes = FALSE, col = gray.colors(n = 1e3))

# Calculated
cols <- hcl.colors(n = 1e3, palette = "YlOrRd", rev = TRUE)
image(agenbag1, axes = FALSE, col = cols)
image(agenbag2, axes = FALSE, col = cols)

Performs Contextual Median Filter

Description

This function implements the Contextual Median Filter (CMF) algorithm, which was first described by Belkin & O'Reilly (2009), following the pseudocode provided in their paper.

Usage

contextualMF(X)

Arguments

Details

Following the definition of CMF, since imagine v.2.0.0, times argument will not be available anymore.

imagine offers the CMF algorithm but for the using to find out oceanographic fronts, it is recommended to see and use the functions of the grec package.

Value

contextualMF returns a matrix object with the same dimensions of X.

References

Belkin, I. M., & O'Reilly, J. E. (2009). An algorithm for oceanic front detection in chlorophyll and SST satellite imagery. Journal of Marine Systems, 78(3), 319-326 (doi:10.1016/j.jmarsys.2008.11.018).

Examples

data(wbImage)

cmfOut <- contextualMF(X = wbImage)

# image(cmfOut)

Make convolution calculations from numeric matrix

Description

This function takes a matrix object, and for each cell multiplies its neighborhood by the kernel. Finally, it returns for each cell the mean of the kernel-weighted sum.

Usage

convolution2D(X, kernel, times = 1, normalize = FALSE, na_only = FALSE)

convolutionQuantile(
  X,
  kernel,
  probs,
  times = 1,
  normalize = FALSE,
  na_only = FALSE
)

convolutionMedian(X, kernel, times = 1, na_only = FALSE)

Arguments

Details

Convolution is a mathematical operation that combines two arrays of numbers to produce an array of the same structure. The output will consist of only valid values, meaning those where both arrays have non-missing data. Consequently, any missing values (NAs) in the input matrix will propagate outwards to the extent of the convolution kernel.

Through normalization, the output of each convolution window is scaled by dividing it by the sum of the absolute values of the kernel (sum(abs(as.numeric(kernel))), disabled by default).

na_only performs two actions at once: (1) it applies the filter only in the positions where the original value is NA and (2) for the rest of the cells, it is replaced with the value of the original matrix.

Value

convolution2D returns a matrix object with the same dimensions of X.

convolutionQuantile uses the kernel but, for each cell, it returns the position of quantile 'probs' (value between 0 and 1).

convolutionMedian is a wrapper of convolutionQuantile with probs = 0.5.

Examples

# Generate example matrix
nRows <- 50
nCols <- 100

myMatrix <- matrix(runif(nRows*nCols, 0, 100), nrow = nRows, ncol = nCols)
kernel <- diag(3)

# Make convolution
myOutput1 <- convolution2D(myMatrix, kernel)
myOutput2 <- convolutionQuantile(myMatrix, kernel, probs = 0.7)

# Plot results
par(mfrow = c(2, 2))
image(myMatrix, zlim = c(0, 100))
image(myOutput1, zlim = c(0, 100))
image(myOutput2, zlim = c(0, 100))

Make a 2D filter calculations from numeric matrix

Description

This functions take a matrix object, and for each cell calculate mean, median or certain quantile around a squared/rectangular neighborhood.

Usage

meanFilter(X, radius, times = 1, na_only = FALSE)

quantileFilter(X, radius, probs, times = 1, na_only = FALSE)

medianFilter(X, radius, times = 1, na_only = FALSE)

Arguments

Details

radius must be defined as a 2-length numeric vector specifying the number of rows and columns of the window which will be used to make calculations. If the length of radius is 1, the window will be a square.

Functions use C++ algorithms for running some statistical calculations. The mean is far obvious, however, there are several ways to perform quantiles. quantileFilter function uses arma::quantile: a RcppArmadillo function, which is equivalent to use R quantile funtion with type = 5.

medianFilter is a wraper of quantileFilter, so the same observations are applied to it.

na_only performs two actions at once: (1) it applies the filter only in the positions where the original value is NA and (2) for the rest of the cells, it is replaced with the value of the original matrix.

Value

A matrix object with the same dimensions of X.

quantileFilter don't use a kernel but, for each cell, it returns the position of quantile 'probs' (value between 0 and 1).

medianFilter is a wrapper of quantileFilter with probs = 0.5.

Examples

# Generate example matrix
nRows <- 50
nCols <- 100

myMatrix <- matrix(runif(nRows*nCols, 0, 100), nrow = nRows, ncol = nCols)
radius <- 3

# Make convolution
myOutput1 <- meanFilter(X = myMatrix, radius = radius)
myOutput2 <- quantileFilter(X = myMatrix, radius = radius, probs = 0.1)
myOutput3 <- medianFilter(X = myMatrix, radius = radius)

# Plot results
par(mfrow = c(2, 2))
image(myMatrix, zlim = c(0, 100), title = "Original")
image(myOutput1, zlim = c(0, 100), title = "meanFilter")
image(myOutput2, zlim = c(0, 100), title = "quantileFilter")
image(myOutput3, zlim = c(0, 100), title = "medianFilter")

Data matrix to be used as example image.

Description

matrix object containing numeric data to plot a image. The photo was taken by the author on 2016.

Usage

wbImage

Format

A matrix with dimensions 1280x720.