1. Morphological Transformations and Histogram Equalization
Dr. Rongzhong Li
CSC391/691 Fall 2016
CameraCalibration
Example Images are from: OpenCV-Python Documentation

2. Input + Neighbor + Rule = Output
Cellular Automaton
Input Neighbor Rule = Output 1 1
&& || 1 1
Growth of Crystal, Replication of bacteria, Complex rules=> randomness, Stephen Wolfram
Conway's
Game of Life

3. Kernel Shape 1 1 1
Complexity?
Optimization?

4. Morphological Transformations
Transform(Origin, Kernel)
Erosion
Dilation
Opening = Erosion + Dilation
Closing = Dilation + Erosion
Morphological Gradient
= diff( Dilation, Erosion)
Top Hat = diff(Origin, Opening)
Black Hat = diff(Origin, Closing)
Original
image
Kernel 1

5. Erosion
A pixel in the original image (either 1 or 0) will be considered 1 only if all the pixels under the kernel is 1, otherwise it is eroded (made to zero).

6. Dilation
Opposite of erosion.
A pixel element is '1' if at least one pixel under the kernel is '1'.

7. 1st Level Combination
Opening
= Erosion + Dilation
Closing
= Dilation + Erosion

8. Morphological Gradient = diff(Dilation, Erosion) Top Hat
2nd Level Combination
Morphological Gradient
= diff(Dilation, Erosion)
Top Hat
= diff(Origin, Opening)
Black Hat
= diff(Origin, Closing)

9. Generating Examples from Scratch
Reduce problem complexity

10. Results of Combinations

11. Histogram Threshold 4
128

12. Histogram Threshold 50

13. Histogram Equalization50
150

14. Enhancing Details