---
title: Neighbourhood image filters
layout: module
prerequisites:
- "[Image pixels](image_pixels)"
objectives:
- Understand the basic principle of a neighbourhood filter
motivation: >
This module explains how image features (objects) can be enhanced using filters
#| | | | | | | | |
#|---|---|---|---|---|---|---|---|
#| NC | NC | NC | | | | | |
#| NC | C, NC | NC | | | | | |
#| NC | NC | NC | | | | | |
#| | | | | NB | NB | NB | |
#| | | | | NB | B, NB| NB | |
#| | | | | NB | NB | NB | |
#| | | | | | | | |
concept_map: >
graph TB
P(pixel) --> |has| NBH(neighbourhood pixels)
NBH --> |are used in| A(mathematical formula)
A --> |compute new| NP(pixel value)
# figure: /figures/binarization.png
# figure_legend: Image before and after binarization by applying a threshold.
activity_preface: >
Use mean filter to facilitate image binarization
activities:
"ImageJ GUI": "filter_neighbourhood/activities/mean_filter_imagejgui.md"
"KNIME": "filter_neighbourhood/activities/mean_filter_knime.md"
# "ImageJ Macro":
# "Jython":
# "MATLAB":
exercises_preface: >
exercises:
# "ImageJ GUI":
# "ImageJ Macro":
# "Jython":
# "MATLAB":
learn_next:
- "[Convolution filters](filter_convolution)"
- "[Rank filters](filter_rank)"
external_links:
---