Color Imaggge Processing

Inel 5327

Prof. Vidya Manian Introduction

• fundamentals • Color models • Histogram processing • ShidhiSmoothing and sharpening • Color image segmentation • Edge detection Color fundamentals

Chromatic spans the EM spectrum from 400nm to 700nm. Quality of light source: Radiance, luminance and brightness Color fundamentals

• Radiance-total amount of energy that flows from the light source (watts, W) • Luminance-amount of energy an observer perceives (lumens , lm) • Brightness-embodies the achromatic notion of intensity • Absorption characteristics of the human eye • =435.8nm , =546 .1nm , =700nm

• Characteristic used to distinguish one color from another – brightness, , and saturation • – hue and saturation taken together • Tristimulus values (X,Y and Z) – amounts of red, green, and blue needed to form any particular color • A color is then specified by

XYZ xyz,, X YZ XYZ  XYZ  • x+y+z=1 • CIE chromaticity diagram – used to specify • For any value of x and y, corresponding value of z (blue) is obtained from Figure. Chromaticity diagram

Color models • Also called or color system-facilitate specification of colors in some standard , accepted way • It is a specification of a coordinate system • RGB

CMY and CMYK color model

• [C M Y]’=[1 1 1]’-[R G B]’ HSI color model

• RGB not good for practical human interpretation • Hue, saturation and brightness –used for dibilbjdescribing a color object • Hue –color attribute describes pure color (pure , or red) • Saturation-measure of degree to which a pure color is diluted byyg light • Brightness-embodies achromatic notion of intensity

load trees yiq=rgb2ntsc(map); [y, i,q] = ind2rgb(X, yiq); figure subplot(2,2,2), imshow(y), title('Luminance'); subplot( (,,),2,2,3), imshow( (),i), title( 'Hue'); subplot(2,2,4), imshow(q), title('Saturation'); subplot(2,2,1), imshow(X,map), title('Original'); • hsv=rgb2hsv(map); • [h,s,v]=ind2rgb(X,hsv); • subplot(2,2,2), imshow(h), title('Luminance'); • subplot(2, 2, 3), imshow(s), title('saturation'); • subplot(2,2,4), imshow(v), title('value'); Converting from RGB to HSI

 if BG B G H   360 if B>G 1 [(RG ) ( RB _ )] 1  2    cos  21/2 [(R  GRBGB ) ( )(  )]  3 SRGB1[min(,,)] ()R  GB 1 I ()RGB 3

Manipulating HSI component images

Normalized RGB color space

R rR,G, B  , R  G  B

G gR,G, B  , R  G  B

B bRGB,,  RGB 

r  g  b 1 Pseudocolor image processing

• IP consists of assigning colors to gray values based on a specified criterion (generate color maps of classification) • Intensity slicing – using a plane at f(x,y)=li to slice ifiimage function to two l llevels

fxy()( , )  ck iff(xy)Vif f(x,y) Vk

ck is the color associated with the kth intensity interval, Vk is defined byyp the partitionin gpg planes at l=k-1 and l=k Intensity slicing

Color coding Intensity to color transformations

Color coding of multispectral image

Basics of full color image processing Color transformations

Perceptually uniform color space CIE L*a*b*

X  0.412453R  0.357580G  0.180423B Y  0.212671R  0.715160G  0.072169B Z  0.019334R  0.119193G  0.950227B

 Y  L*  116 f   16  Yn    X   Y  a*  500 f    f     X n   Yn    Y   Z  b*  200 f    f     Yn   Zn 

q1/ 3, if q  0.008856 f (q)   7.787q 16/116, otherwise The tristimulus values Xn, Yn, Zn are those of the nominally white object-color stimulus given by a CIE standard illuminant Tone and color corrections

Histoggpram processin g

• Spread the color intensities uniformly, leaving the unchangg(ed (HSI s pace ) Color image smoothing

• Average of RGB component vectors in the neighborhood is • Smooth only the intensity component of the HSI representation 1 cxy(, )  cxy (, ) K (,)xy Sxy 1  Rxy(, ) K   (,)xy Sxy   1  cxy()( , )(  Gxy (), )  K (,)xy Sxy    1  Bxy(, ) K  ()( xy, )S xy

Color image sharpening

• The Laplacian of vector c is 2R()(xy, ) 22  [(,)]cxy  Gxy (,)  2   B(,xy )

Compppute Laplacian of a full-color imaggye by comp uting Laplacian of each component image separately

Image segmentation based on color • Segmentation in HSI color space

Color segmentation in RGB vector space • a –estimate of the “average” color that we wish to segment • Euclidean distance between an arbitrary point z in RGB space and a Dza(, ) z a [(z aza )T (  )]1/2 2221/2 [(zaRR ) ( za GG ) ( za BB ) ]

The locus of points such that D(z,a)D0 is a solid sphere of radius D0 A useful generalization to the distance measure C is the covariance matrix of the samples representative of the color we wish to segment Dza()[()( , )[() z aT C11/2 ( z a )]

Color edge detection (vector-valued images) • For scalar images -gradient is a vector pointing in the direction of maximum rate of change of f at coordinates (x,y) • DiZenzo, S. [1986]. “A note on the gradient of a multi - image”, CVGIP, Vol. 33, pp. 116-125. • r,g,b –unit vectors along the R, G, and B axis of RGB color space, dfidefine vectors R GB uggb x xx R GB vggb yyy • Define gxx, gyy and gxy in terms of the dot product of vectors u and v as: RRR222 guuuu. T    xx xxx 222 RRR gvvvv. T    yy yyy R RGGBB guvuv. T    xy x yxyxy • It can be shown that (DiZenzo[1986]) the direction of maxifhf()iibhlimum rate of change of c(x,y) is given by the angle

1 1  2gxy    tan   2(ggxxyy ) • This produces 2 values 90apart, associates with each point (x,y) a pair of orthogonal directions, along one direction F is maximum, and along the other F is minimum • The value of the range of change at (x,y), is given by

1/2 1  Fggggg()  (xx yy ) ( xx yy )cos2 2 xy sin2 2  

Noise in color images

Color image compression Exercises • In an automated assembly application, three classes of parts are to be color coded in order to simplify detection. However, only a monochrome TV camera is available to acquire digital images. Propose a technique for using this camera to detect the 3 different colors. • How would you implement the color equivalent of gray scale histogram matching from chapter 3 . • Show that Dza()[()( , )[() z aT C11/2 ( z a )] reduces to Dza(, ) z a [(za )T ( za  )]1/2 2221/2 [(zaRR ) ( za GG ) ( za BB ) ] When C=I (identity matrix).