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# isfilterseparable

Determine whether filter coefficients are separable

## Syntax

```S = isfilterseparable(H)[S, HCOL, HROW] = isfilterseparable(H)```

## Description

`S = isfilterseparable(H)` takes in the filter kernel `H` and returns `1` (true) when the filter is separable, and `0` (false) otherwise.

```[S, HCOL, HROW] = isfilterseparable(H)``` uses the filter kernel, `H`, to return its vertical coefficients `HCOL` and horizontal coefficients `HROW` when the filter is separable. Otherwise, `HCOL` and `HROW` are empty.

## Input Arguments

 `H` `H` numeric or logical, 2-D, and nonsparse.

## Output Arguments

 `HCOL` `HCOL` is the same data type as input `H` when `H` is either single or double floating point. Otherwise, `HCOL` becomes double floating point. If `S` is true, `HCOL` is a vector of vertical filter coefficients. Otherwise, `HCOL` is empty. `HROW` `HROW` is the same data type as input `H` when `H` is either single or double floating point. Otherwise, `HROW` becomes double floating point. If `S` is true, `HROW` is a vector of horizontal filter coefficients. Otherwise, `HROW` is empty. `S` Logical variable that is set to `true`, when the filter is separable, and `false`, when it is not.

## Examples

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Determine if the Gaussian filter created using the `fspecial` function is separable.

Create a Gaussian filter.

```twoDimensionalFilter = fspecial('gauss'); ```

Test the filter.

```[isseparable,hcol,hrow] = isfilterseparable(twoDimensionalFilter) ```
```isseparable = logical 1 hcol = -0.1065 -0.7870 -0.1065 hrow = -0.1065 -0.7870 -0.1065 ```

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### Separable two dimensional filters

Separable two-dimensional filters reflect the outer product of two vectors. Separable filters help reduce the number of calculations required.

A two-dimensional convolution calculation requires a number of multiplications equal to the width $×$ height for each output pixel. The general case equation for a two-dimensional convolution is:

`$Y\left(m,n\right)=\sum _{k}\sum _{l}H\left(k,l\right)U\left(m-k,n-l\right)$`

If the filter H is separable then,

`$H\left(k,l\right)={H}_{row}\left(k\right){H}_{col}\left(l\right)$`

Shifting the filter instead of the image, the two-dimensional equation becomes:

`$Y\left(m,n\right)=\sum _{k}{H}_{row}\left(k\right)\text{ }\sum _{l}{H}_{col}\left(l\right)\text{ }\text{ }U\left(m-k,n-l\right)$`

This calculation requires only (width + height) number of multiplications for each pixel.

### Algorithms

The `isfilterseparable` function uses the singular value decomposition `svd` function to determine the rank of the matrix.