Title: Figures from Gonzalez and Woods, Digital Image Processing, Second Edition, 2002.
1Lecture 17
- Figures from Gonzalez and Woods, Digital Image
Processing, Second Edition, 2002.
2Psuedocolor Processing
- Produce RGB image (usually) from gray levels.
- Also possible to transform a number of monochrome
images.
3Chapter 6 Color Image Processing
4Chapter 6 Color Image Processing
5Typical Transformations
- Sinusiodal functions with differing start points
6Chapter 6 Color Image Processing
7Chapter 6 Color Image Processing
8Chapter 1 Introduction
9Chapter 1 Introduction
10Chapter 6 Color Image Processing
11Chapter 6 Color Image Processing
12Color Transformation
13Chapter 6 Color Image Processing
14Color Transformations (2)
- Also possible in
- CMYK components
- HSI components
15Chapter 6 Color Image Processing
16Modify through intensity
17Chapter 6 Color Image Processing
18The Wheel of Hues
- Notice how complementary colors are opposite one
another
19Chapter 6 Color Image Processing
20Effect of Complementary Transformation
- Notice difference in RGB vs. HSI model
21Chapter 6 Color Image Processing
22Color Slicing
23Chapter 6 Color Image Processing
24Chapter 6 Color Image Processing
25Chapter 6 Color Image Processing
26Histogram Processing
- Done to intensity component. Should not be done
to RGB components. - Next figure
- Histogram equalization to intensity
- Then increased saturation
27Chapter 6 Color Image Processing
28Smoothing and Sharpening
- Smoothing filters (like averaging or low pass)
- Or sharpening (like Laplacian or high pass)
- Are usually done on R,G,B components
- Or on intensitywith slightly different results
29Chapter 6 Color Image Processing
30Chapter 6 Color Image Processing
31Chapter 6 Color Image Processing
32Chapter 6 Color Image Processing
33Color Segmentation
- As done in HSI space
- Color represented by hue image
- Saturation used to perform masking
- Intensity contains no color information and not
used much for segmentation
34Chapter 6 Color Image Processing
35Color Segmentation
- Done with distances in RGB space
- Either Euclidean or maximum are easy to do
- In fact, looking at this example, my thought was,
oh, thats what you want!
36Chapter 6 Color Image Processing
37Chapter 6 Color Image Processing
38Color Noise
- Handled naturally in RGB mode.
- Standard color models make sense
39Chapter 6 Color Image Processing
40Chapter 6 Color Image Processing
41Chapter 6 Color Image Processing
42Chapter 6 Color Image Processing
43Chapter 6 Color Image Processing