LocalFilters.jl

Multi-dimensional local filters (convolution, mathematical morphology, etc.) for Julia.
Author emmt
Popularity
13 Stars
Updated Last
1 Year Ago
Started In
March 2017

LocalFilters.jl

License Build Status Code Coverage

This package implements multi-dimensional local filters for Julia (convolution, mathematical morphology, etc.). Local filters are defined by specific operations combining each value of a source array with the values in a local neighborhood which may have any size, shape and dimensionality. Predefined local filters are provided as well as means to simply implement custom filters.

This document is structured as follows:

Note that this is a first implementation to define the API. It is is reasonably fast (see benchmarks.jl) but separable kernels can be made faster.

Packages with overlapping functionalities:

  • ImageFiltering for local filters on multidimensional arrays (not just images), also implement various boundary conditions;

  • ImageMorphology for fast morphological operations with separable structuring elements;

Summary

LocalFilters implements local filtering operations which combine the values of an array in a neighborhood of each elements of the array (and possibly the values of a kernel associated with the neighborhood). The neighborhood is defined relatively to a given position by an instance of a type derived from Neighborhood. For mathematical morphology operations, a neighborhood is called a structuring element.

Denoting A the source array and B the neighborhood or the kernel (by default B is a centered box of size 3 along every dimension), the available filters are:

  • erode(A, B=3) performs an erosion (local minimum) of A by B;

  • dilate(A, B=3) performs a dilation (local maximum) of A by B;

  • localextrema(A, B=3) yields the erosion and the dilation of A by B;

  • opening(A, B=3) performs an erosion followed by a dilation;

  • closing(A, B=3) performs a dilation followed by an erosion;

  • top_hat(A, B=3 [, S]) performs a summit detection (an optional third argument S may be supplied to pre-smooth A by S);

  • bottom_hat(A, B=3 [, S]) performs a valley detection (an optional third argument S may be supplied to pre-smooth A by S);

  • localmean(A, B=3) performs a local averaging;

  • convolve(A, B=3) performs a convolution by the kernel B or by the support of B is eltype(B) is Bool;

  • bilateralfilter(A, Fr, Gs) performs a bilateral filtering of array A with Fr the range kernel for smoothing differences in intensities and Gs the spatial kernel for smoothing differences in coordinates. Alternatively one can specify the range and spatial parameters bilateralfilter(A, σr, σs) for default Gaussian kernels. (See: https://en.wikipedia.org/wiki/Bilateral_filter.)

and many more to come...

Implementation

General local filters

The pseudo-code for a local filtering operation C = filter(A, B) writes:

for i  Sup(A)
    v = initial(A[i])
    for j  Sup(B) and such that i-j  Sup(A)
        v = update(v, A[i-j], B[j])
    end
    store(C, i, v)
end

where A is the source of the operation, B is the neighborhood, C is the result of the operation. Here Sup(A) denotes the support of A (that is the set of indices in A). The methods initial, update and store and the type of the variable v are specific to the considered operation. For maximum efficiency, the methods initial and update shall return the same type of result. To execute the filter, call the localfilter! method as:

localfilter!(dst, A, B, initial, update, store)

localfilter! applies the local filter defined by the neighborhood B and methods initial, update and store to the source array A and stores the result in the destination dst which is then returned.

For instance, to compute a local maximum (i.e. a dilation in mathematical morphology terms):

initial(a) = typemin(typeof(a))
update(v,a,b) = (b && v < a ? a : v)
store(C,i,v) = C[i] = v

with typeof(a) the type of the elements of A. Of course, anonymous functions can be exploited to implement this filter in a single call:

localfilter!(dst, A, B,
             (a)     -> typemin(typeof(a)),   # initial method
             (v,a,b) -> (b && v < a ? a : v), # update method
             (C,i,v) -> C[i] = v)             # store method

Below is another example of the methods needed to implement a local average:

initial(a) = (0, zero(T))
update(v,a,b) = v[1] + 1, v[2] + (b ? a : zero(T))
store(C,i,v) = C[i] = v[2]/v[1]

with T = typeof(a).

The same mechanism can be used to implement other operations such as convolution, median filtering, etc. via the localfilter! driver.

Fast separable local filters

When the filter amounts to combining all elements in a rectangular neighborhood by an associative binary operation (+, min, max, etc.), the van Herk-Gil-Werman algorithm can be used to implement the filter. This algorithm is much faster than a naive implementation (about 3N operations per element for a N-dimensional array whatever the size of the neighborhood instead of p^N - 1 operations for a neighborhood of lenght p along all the N dimensions). Another advantage of the van Herk-Gil-Werman algorithm is that it can be applied in-place. Such a filter is said to be separable and can be applied along each dimension, one at a time.

The syntax to apply a separable local filter is:

localfilter!([dst = A,] A, dims, op, rngs [, w])

which overwrites the contents of dst with the result of applying van Herk-Gil-Werman algorithm to filter array A along dimension(s) dims with (associative) binary operation op and contiguous structuring element(s) defined by the interval(s) rngs. Optional argument w is a workspace array which is automatically allocated if not provided; otherwise, it must be a vector of same element type as A which is automatically resized as needed. The destination dst must have the same indices as the source A (i.e. axes(dst) == axes(A) must hold). Operation can be done in-place; that is, dst and A can be the same (this is the implemented behavior if dst is not supplied).

Argument dims specifies along which dimension(s) of A the filter is to be applied, it can be a single integer, several integers or a colon : to specify all dimensions. Dimensions are processed in the order given by dims (the same dimension may appear several times) and there must be a matching interval in rngs to specify the structuring element (except that if rngs is a single interval, it is used for every dimension in dims). An interval is either an integer or an integer unit range in the form kmin:kmax (an interval specified as a single integer, say k, is the same as specifying k:k).

Assuming mono-dimensional arrays A and dst, the single filtering pass:

localfilter!(dst, A, :, op, rng)

yields:

dst[j] = A[j-kmax] ⋄ A[j-kmax+1] ⋄ A[j-kmax+2] ⋄ ... ⋄ A[j-kmin]

for all j ∈ [first(axes(A,1)):last(axes(A,1))], with x ⋄ y = op(x, y), kmin = first(rng) and kmax = last(rng). Note that if kmin = kmax = k (which occurs if rng is a simple integer), the result of the filter is to operate a simple shift by k along the corresponding dimension and has no effects if k = 0. This can be exploited to not filter some dimension(s).

For instance:

localfilter!(A, :, min, -3:3)

overwrites A with its morphological erosion (local minimum) on a centered structuring element of width 7 in every dimension.

Another example, assuming A is a three-dimensional array:

localfilter!(A, :, max, (-3:3, 0, -4:4))

overwrites A its morphological dilation (i.e. local maximum) in a centered local neighborhood of size 7×1×9 (nothing is done along the second dimension). The same result may be obtained with:

localfilter!(A, (1,3), max, (-3:3, -4:4))

where the second dimension is omitted from the list of dimensions. The out-place version, allocates the destination array and is called as:

localfilter([T,] A, dims, op, rngs [, w])

with T the element type of the result (by default T = eltype(A)).

References

  • Marcel van Herk, "A fast algorithm for local minimum and maximum filters on rectangular and octagonal kernels" in Pattern Recognition Letters 13, 517-521 (1992).

  • Joseph Gil and Michael Werman, "Computing 2-D Min, Median, and Max Filters" in IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 504-507 (1993).

Neighborhoods

Neighborhood (a.k.a. structuring element for the adepts of mathematical morphology) is a central concept in LocalFilters. The neighborhood defines which values are involved in a local operation for each component of the source array. Neighborhoods are assumed to be shift invariant but may have any support shape and may have embedded weights (e.g., to implement local convolution).

Types of neighborhoods

From the user point of view, there are three kinds of neighborhoods:

  • Rectangular boxes are rectangular neighborhoods whose edges are aligned with the axes of array indices and which may be centered or have arbitrary offsets along the dimensions. These neighborhoods are represented by instances of LocalFilters.RectangularBox.

  • Arbitrarily shaped neighborhoods are neighborhoods with arbitrary shape and offset. These neighborhoods are represented by instances of LocalFilters.Kernel with boolean element type. These neighborhoods are constructed from an array of booleans and an optional starting index.

  • Kernels are neighborhoods whose elements are weights and which may have arbitrary offset. These neighborhoods are represented by instances of LocalFilters.Kernel with numerical element type. These neighborhoods are constructed from an array of weights and an optional starting index.

Syntax for neighborhoods

  • The default neighborhood is a centered rectangular box of width 3 in each of its dimensions.

  • A scalar integer w yields a centered rectangular box of size w along all dimensions. w must be at least equal to 1 and the geometrical center of the box is defined according to the conventions in fftshift.

  • A tuple t of integers yields a centered rectangular box whose size is t[i] along the i-th dimension. All values of t must be larger or equal to 1. Tip: Remember that you can use v... to convert a vector v into a tuple.

  • An array A yields a LocalFilters.Kernel whose coefficients are the values of A and whose neighborhood is the centered bounding-box of A.

  • A Cartesian region R (an instance of CartesianIndices) yields a LocalFilters.RectangularBox which is a rectangular neighborhood whose support contains all relative positions within first(R) and last(R).

  • A rectangular box neighborhood created by calling LocalFilters.RectangularBox as:

    LocalFilters.RectangularBox(R)
    LocalFilters.RectangularBox(I1, I2)
    LocalFilters.RectangularBox(dims, offs)
    LocalFilters.RectangularBox(inds)

    where R is an instance ofCartesianIndices, I1 and I2 are two CartesianIndex specifying the first and last relative position within the neighborhood, dims and offs are tuples of integers specifying the dimensions of the neighborhood and its offsets, inds are unit ranges.

    Assuming dim is an integer, then:

    LocalFilters.RectangularBox{N}(dim)

    yields an N-dimensional rectangular box of size dim along all dimensions and centered at the geometrical center of the box (with the same conventions as fftshift).

    Similarly, assuming i1 and i2 are integers, then:

    LocalFilters.RectangularBox{N}(i1:i2)

    yields an N-dimensional rectangular box with index range i1:i2 along all dimensions.

Methods on a neighborhood

The following statements make sense on a neighborhood B:

eltype(B) -> element type of B
ndims(B)  -> number of dimensions of B
length(B) -> number of elements in the bounding-box of B
size(B)   -> size of the bounding-box of B along all dimensions
size(B,d) -> size of the bounding-box of B along d-th dimension
first(B)  -> CartesianIndex of first position in the bounding-box
             of B relative to its anchor
last(B)   -> CartesianIndex of last position in the bounding-box
             of B relative to its anchor
B[i]      -> yields the kernel value of B at index i

Note that the index i in B[i] is assumed to be between first(B) and last(B), for efficiency reasons this is not checked. The type returned by eltype(B) is Bool for a neighborhood which is just defined by its support (e.g. a LocalFilters.CenteredBox or a LocalFilters.RectangularBox), the element type of its kernel otherwise.

CartesianIndices(B)

yields the Cartesian indices of relative positions of the bounding-box of neighborhood B.

If the argument B which defines a neighborhood (see previous section) is not an instance of a type derived from LocalFilters.Neighborhood, it may be explicitly converted by:

convert(LocalFilters.Neighborhood{N}, B)

with N the number of dimensions of the target array.

Installation

To install the last official version:

using Pkg
Pkg.add("LocalFilters")

To use the last development version, install with Pkg, the Julia package manager, as an unregistered Julia package (press the ] key to enter the Pkg REPL mode):

... pkg> add https://github.com/emmt/LocalFilters.jl.git

The LocalFilters package is pure Julia code and there is nothing to build.