1. Digital Image Processing Lecture 15: Morphological Algorithms
Prof. Charlene Tsai

2. Before Lecture …
Solution to previous quiz: all erosions

3. Overview
Extracting image components that are useful in the representation and description of shape.
We’ll consider:
Boundary extraction
Region filling
Extraction of connected components
Skeletonization

4. Boundary Extraction Denote the boundary of set A by
Step1: eroding A by the structuring element B
Step2: taking the difference between A and its erosion.

5. Using B as the structuring element, so boundary is 1 pixel thick.
Another Illustration
Using B as the structuring element, so boundary is 1 pixel thick.

6. Region Filling
A is a set containing a subset whose elements are 8-connected boundary points of a region.
Goal: fill the entire region with 1’s.

7. Region Filling (con’d)
Final
Terminate when Xk=Xk-1

8. Some Remarks
The dilation process would fill the entire area if left unchecked.
limits the result to inside the region of interest.
Conditional dilation
Applicable to any finite number of such subsets, assuming that a point inside each boundary is given.

9. Connected Components
Extraction of connected components in a binary image is central to many automated image analysis applications.
Let Y be a connected component in set A and p a point of Y.

10. Application of Connected Component

11. Skeletonization
We have seen some algorithms for skeletonization when discussing topology.
Review: skeleton of a binary object is a collection of lines and curves that describe the size and shape of the object.
Different algorithms and many possible different skeletons of the same object.
Here we use a combination erosion and opening operations

12. (con’d) Formulation: with where
The big K is the last iterative step before A erodes to an empty set.
k time

13. Skeletonization: Demo
Final skeleton

14. Grayscale Morphology – Dilation (Advance)
General formulation:
where f is the grayscale image and b is the structuring element.
In other words, the value of dilation at (x,y) is the maximum of the sum of f and b in the interval spanned by b.

15. Example
(x) 10 20 30 40 50 60 70 1 2 3 4 5
(y) 1 2 3 4 5 6 7 8 9 -1 1 b f
What is ?

16. Effect of Dilation Two effects:
If all values of the structuring element are positive, the output image tends to be brighter.
Dark details either are reduced or eliminated, depending on how their values and shapes relate to the structuring element.

17. Grayscale Morphology - Erosion
(x) 10 20 30 40 50 60 70 1 2 3 4 5
(y) 1 2 3 4 5 6 7 8 9 -1 1 b f
What is ?

18. Demo

19. Opening & Closing
Opening:
Closing:

20. Demo
original

21. Application: Granulometry
Definition: determining the size distribution of particles in an image
Useful when objects are overlapping and clustered.
Detection of individual particles are hard.

22. (con’d)
Opening operations with structuring elements of increasing size are performed on the original image.
Motivation: opening operations of a particular size have the most effect on regions containing particles of similar size.
Method:
For every structuring element, compute the difference btw the Im-1 and Im, where m is the index of the structuring element.
At the end, normalize the differences => a histogram of particle-size distribution

23. Demo