1. 1 Vladimir Botchko botchko@lut.fi Lecture 5. Color Image Processing Lappeenranta University of Technology (Finland)

2. 2 Color Image Processing n Fundamentals n Color models n Pseudocolor image processing n Full color image processing n Color transformations n Smoothing and Sharpening n Color segmentation n Noise in color images n Color image compression n Multispectral image processing

3. 3 Fundamentals n Colors in the visible range of wavelengths (upper left), mixtures of light (additive primaries) (upper right) and color bars used in analysis.

4. 4 Color models n Relative color gamuts of a dipslay and a printer in XYZ chromaticity coordinate system (right). n Left - XYZ color space.

5. 5 Color Image Processing n Fundamentals n Color models n Pseudocolor image processing n Full color image processing n Color transformations n Smoothing and Sharpening n Color segmentation n Noise in color images n Color image compression n Multispectral image processing

6. 6 Color models n http://cvision.ucsd.edu/index.htm

7. 7 Color models n RGB system, HSI (or HSV) system (right) (I-intensity, V value)

8. 8 Color models n Three match curves. RGB system (CIE 1931)(left), XYZ system (CIE 1931)(right)

9. 9 Color models n RGB space. The right image is a rotated left image (for correspondence: BL is black, W is white).

10. 10 Hue, saturation, intensity system

11. 11 Color models n Chromaticity

12. 12 Color models n Multitriangle representation (left) n Luminance, chromaticity (right)

13. 13 Color models n Karhunen-Loev system

14. 14 Color Image Processing n Fundamentals n Color models n Pseudocolor image processing n Full color image processing n Color transformations n Smoothing and Sharpening n Color segmentation n Noise in color images n Color image compression n Multispectral image processing

15. 15 Pseudocoloring n Myocardial perfusion study. Left is a heart attack (blue region increased), right is normal.

16. 16 Pseudocoloring. X-rays. nana

17. 17 Pseudocoloring n Right – three images: elevation relief (upper left), the color coded magnetic field (higher values are yellowish) (upper right), the composition of first two. Left – underpainting revealed through color dipslay (Prof. L. MacDonald, Derby University,GB).

18. 18 Thematic classification of six-band satellite imagery using a minimum distance classifier

19. 19 Color Image Processing n Fundamentals n Color models n Pseudocolor image processing n Full color image processing n Color transformations n Smoothing and Sharpening n Color segmentation n Noise in color images n Color image compression n Multispectral image processing

20. 20 Painting Restoration. A Queen house, London. The part of painting was copied from another painting (upper right) and used for restoration of the lost painting part.

21. 21 Color Image Processing n Fundamentals n Color models n Pseudocolor image processing n Full color image processing n Color transformations n Smoothing and Sharpening n Color segmentation n Noise in color images n Color image compression n Multispectral image processing

22. 22 Color segmentation n Image segmentation based on color feature: burnt forest area, forest fire, dead forest (brown).

23. 23 Color Image Processing n Fundamentals n Color models n Pseudocolor image processing n Full color image processing n Color transformations n Smoothing and Sharpening n Color segmentation n Noise in color images n Color image compression n Multispectral image processing

24. 24 Color image compression. n Original color image (upper left), compressed image (upper right), error histogram in compression (the error is a delta E – the smallest color noticible difference) and error image (large error values are white).

25. 25 Color Image Processing n Fundamentals n Color models n Pseudocolor image processing n Full color image processing n Color transformations n Smoothing and Sharpening n Color segmentation n Noise in color images n Color image compression n Multispectral image processing

26. 26 Using a ratio image to enhance road detail (two upper is a multispectral image components) n The third image (lower) is the dividend of the first two

27. 27 Color analysis. Color similarity n Brick n Ceramic tiles n Wooden pieces n Car parts

28. 28 Color analysis. The Munsell Book of Color contains a set of color patches n http://www.it.lut.fi/research/color/demonstration/demonstration.html

29. 29 Color analysis. Metameric spectra. Color is the same at one illumination (left patches) and different at another illumination (right patches).

30. 30

31. 31 Statistical Analysis of Natural Images Upper curve is mean, lower curve is standard deviation

32. 32 http://www.techexpo.com/WWW/opto-knowledge/ for previous picture site. http://stargate.jpl.nasa.gov/lctf/ for this picture site.