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Artifact ID: | 046063cc30c05d1f56969abf6363e7127beffcb7 |
---|---|

Page Name: | ConvolutionMatrix |

Date: | 2013-09-01 15:48:26 |

Original User: | user |

Parent: | 7beaaa3db5f7291ec92685ad78e896523dbad911 |

# Convolution Matrix

It's classical image processing method: applying of 2D filter on each pixel. More about convolution matrix read this. Main idea is the same as in other filters: each original samples gives some value to common result but all of thees values has different amount which depend of filter formula.

There are 2 main notes on this filter. First: no recursion, so original pixels (samples) - are real original and temporary clean copy of original image should be allocated and used for calculation. Second: normalization of result is need. Result is divided by special value (**div**) and shifted by another (**shift** or **bias**). And if now it's lesser then 0, 0 should be used instead of, if bigger then 255, 255 should be placed as result.

See test4.scm, test6.scm, test7.scm. Different predefined convolution matrix with div and shift are available in conv.scm.

Some examples are here:

Edge detection matrix after black and white conversion:

Dilation matrix application after conversion to black and white:

Edge detection matrix with div=3 and shift=60:

Emboss matrix applied: