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Color Image Analysis

Color Image Analysis. Chaur-Chin Chen Institute of Information Systems and Applications Department of Computer Science National Tsing Hua University E-mail: cchen@cs.nthu.edu.tw Tel: +886 3 573 1078. Color Image Processing. Three Primary Signals : Red , Green , Blue

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Color Image Analysis

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  1. ColorImageAnalysis Chaur-Chin Chen Institute of Information Systems and Applications Department of Computer Science National Tsing Hua University E-mail: cchen@cs.nthu.edu.tw Tel: +886 3 573 1078

  2. Color Image Processing • Three Primary Signals: Red, Green, Blue • Representation of (R,G,B) Color Images • A Palette of 256 colors from HP Hex Triplet Color Chart • RGB and HSI Conversion • RGB and YIQ Conversion

  3. Color Images and Their Histograms

  4. Color Images and Their Histograms

  5. Koala and Its RGB Components

  6. (R,G,B) Histograms of Koala

  7. (R,G,B) Histograms of Starfruits

  8. (R,G,B) Histograms of Greentrees

  9. From JPEG to RGB • m=512; n=512; npixel=m*n; • I=imread('koala512.jpg'); % m * n * 3 = (R,G,B) • R=I(:,:,1); G=I(:,:,2); B=I(:,:,3); • hR=zeros(256); hG=zeros(256); hB=zeros(256); • for i=1:256 • for j=1:256 • r=1+R(i,j); g=1+G(i,j); b=1+B(i,j); • hR(r)=hR(r)+1; • hG(g)=hG(g)+1; • hB(b)=hB(b)+1; • end • end • for k=1:256 • hR(k)=100.0*(hR(k)/npixel); • hG(k)=100.0*(hG(k)/npixel); • hB(k)=100.0*(hB(k)/npixel); • end • subplot(2,2,1) • imshow(R) • subplot(2,2,2) • imshow(G) • subplot(2,2,3) • imshow(B) • L=0:255; • subplot(2,2,4) • plot(L,hR,'r-',L,hG,'g-',L,hB,'b-') • title('RGB Histograms of 512 \times 512 Koala512') • xlabel('Intensity Levels') • ylabel('Percentage %')

  10. Red = FF0000 Green = 00FF00 Blue = 0000FF Cyan = 00FFFF Magenta= FF00FF Yellow = FFFF00 RGB Hex Triplet Color Chart

  11. Hue, Saturation, Intensity (HSI) • Hue is a color attribute that describes a pure color, e.g., pure orange, pure green, pure cyan, whereas Saturation is a measure of the degree to which a pure color is diluted by a white light. Brightness (Intensity) is a subjective description that is practically impossible to measure. • HSI is an ideal tool for developing image processing algorithms based on color description that are natural intuitive to humans.

  12. RGB ←→ HSI • I = (R+G+B)/3 • S=1-3*min{R,G,B}/(R+G+B) =1-min{R,G,B}/I • H=θ if B ≤ G, = 2π- θ if B >G, where Θ=cos-1 {0.5[(R-G)+(R-B)]/[(R-G)2+(R-B)(G-B)]1/2} • B=I*(1-S) • R=I*{1+S*cos(H)/cos[(π/3)-H]} • G=1-(R+B)

  13. RGB ←→ YIQ • Convert (R,G,B) signals into uncorrelated (Y,I,Q) components for further processing and analysis, for example, compression • Y=0.299R + 0.587G + 0.114B • I =0.596R - 0.275G - 0.321B • Q=0.212R – 0.523G + 0.312B

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