1. Digital Image Processing Lecture 26: Color Processing
Prof. Charlene Tsai

2. Color Model (Review) We’ll focus on RGB and HSI models in this lecture
RGB: Red-Gree-Blue model for color monitors and color video cameras
HIS: Hue-Intensity-Saturation model. Color and gray-scale information decoupled, so suitable for many existing gray-scale techniques.

3. RGB Model
HIS Model

4. Exercise (RGB->HIS)

5. Color Transformations
Techniques that process the components of a color image within the context of a single color model, as apposed to conversion between models.
Techniques of interest
Color complements
Color slicing
Histogram processing

6. Color Complements Analogous to gray-scale negatives
Similar to conventional color film negatives
Directly opposite of another on the color circle

7. Color Slicing Highlighting a specific range of colors in an image
Separating object from their surroundings
The simplest is to define the range of interest by a cube, or a sphere
for sphere

8. Color Slicing - Example
sphere
cubic

9. Histogram Equalization
Review: producing an image with an uniform histogram of intensity values.
How to go about doing it?
Erroneous if performing HE on individual color component.
More logical in HIS space
Hue and saturation unchanges
HE on color intensity

10. Histogram Equalization - Example
Before HE, median=0.36
After HE, median=0.5

11. Smoothing – Neighborhood Averaging
RGB: each component can be smoothed independently
HIS: smoothing only the intensity component (so more efficient)

12. Sharpening – Laplacian Enhancement
RGB: computing the Laplacian of each component independently
HIS: Computing only the Laplacian of the intensity component

13. Color Segmentation in RGB
Works better than HIS model
more systematic,
less application-dependent
Given a set of sample colors of interest:
compute the average vector a
for each pixel, determine if the color is in specified vicinity D0 of a
the similarity measure is the Euclidean distance
Very similar to color slicing

14. (con’t)
C is the covariance matrix of the samples. If C=I , D(z,a) is reduced to |z-a| (the Euclidean distance).
Reducing computation

16. Color Edge Detection
There are many ways of doing edge detection on color images
Method I: generating the gradient information on individual planes and combining the results
Method II: computing the gradient of vector c at any point (x,y)

17. Color Edge Detection: Method II
Let r, g, and b be the unit vector along the R,G, and B axis of the RGB color space
Define the vectors:
Let the quantities gxx, gyy, and gxy be

18. (con’t)
The direction and magnitude of max rate of change of c(x,y) is

19. Result of using Method I

20. Method II
Diff.
Method I