Morphological image processing
What will we learn?
- What is mathematical morphology and how is it used in image processing?
- What are the main morphological operations and what is the effect of applying them to binary and grayscale images?
- What is a structuring element (SE) and how does it impact the result of a morphological operation?
- What are some of the most useful morphological image processing algorithms?
Mathematical morphology is a branch of image processing that has been successfully used to provide tools for representing, describing, and analyzing shapes in images. It was initially developed by Jean Serra in the early 1980s [SC82] and—because of its emphasis on studying the geometrical structure of the components of an image—named after the branch of biology that deals with the form and structure of animals and plants. In addition to providing useful tools for extracting image components, morphological algorithms have been used for pre- or postprocessing the images containing shapes of interest.
The basic principle of mathematical morphology is the extraction of geometrical and topological information from an unknown set (an image) through transformations using another, well-defined, set known as structuring element. In morphological image processing, the design of SEs, their shape and size, is crucial to the success of the morphological operations that use them.
The IPT in MATLAB has an extensive set of built-in morphological functions, ...