Color Image Processing

Transcription

1 Color Image Processing Inel 5327 Prof. Vidya Manian

2 Introduction Color fundamentals Color models Histogram processing Smoothing and sharpening Color image segmentation Edge detection

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

4 Color fundamentals Radiance-total amount of energy that flows from the light source (watts,w) Luminance-amount of energy an observer perceives (lumens,lm) lm) Brightness-embodies the achromatic notion of intensity Absorption characteristics of the human eye Blue=435.8nm, green=546.1nm, red=700nm

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8 Characteristic used to distinguish one color from another brightness, hue, and saturation Chromaticity 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 X Y Z x, y, z X Y Z X Y Z X Y Z x+y+z=1 CIE chromaticity diagram used to specify colors For any value of x and y, corresponding value of z (blue) is obtained from Figure.

9 Chromaticity diagram

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

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15 CMY and CMYK color model [C M Y] =[1 1 1] -[R G B]

16 HSI color model RGB not good for practical human interpretation Hue, saturation and brightness used for describing a color object Hue color attribute describes pure color (pure yellow, orange or red) Saturation-measure of degree to which a pure color is diluted by white light Brightness-embodies achromatic notion of intensity

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20 load trees yiq=rgb2ntsc(map); [y,i,q] 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');

21 hsv=rgb2hsv(map); [h,s,v]=ind2rgb(x,hsv); subplot(2,2,2), imshow(h), title('luminance'); subplot(2,2,3), 2 imshow(s), title('saturation'); subplot(2,2,4), imshow(v), title('value');

22 Converting from RGB to HSI if B G H 360 if B>G 1 [( R G ) ( R _ B )] 1 cos 2 2 1/2 [( R G) ( RB)( GB)] 3 S 1 [min( R, G, B )] ( R G B) 1 I ( RGB ) 3

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24 Manipulating HSI component images

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26 Normalized RGB color space,,, B G R R B G R r,,, B G R G B G R g,, B b R G B R G B 1 b g r g

27 Pseudocolor image processing False color 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 image function to two levels l f ( x, y) c k if f(x,y) V k c k is the color associated with the kth intensity interval, V is defined by the partitioning gplanes at l=k-1 and l=k k

28 Intensity slicing

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31 Color coding

32 Intensity to color transformations

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36 Color coding of multispectral image

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38 Basics of full color image processing

39 Color transformations

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44 Perceptually uniform color space Perceptually uniform color space CIE L*a*b* B G R Z B G R Y B G R X Y n Y f L * n n Z f Y f b Y Y f X X f a 200 * 500 * n n Z f Y f otherwise 16 /116, q if, ) ( 3 1/ q q q f The tristimulus values Xn, Yn, Zn are those of the nominally white object-color stimulus given by a CIE standard illuminant

45 Tone and color corrections

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47 Histogram processing Spread the color intensities uniformly, leaving the hues unchanged (HSI space)

48 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, ) S xy 1 Rxy (, ) K ( xy, ) S xy 1 cxy (, ) Gxy (, ) K ( xy, ) S xy 1 Bxy (, ) K ( xy, ) S xy

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52 Color image sharpening The Laplacian of vector c is 2 R( ( xy, ) [ cxy (, )] Gxy (, ) 2 B( xy, ) 2 2 Compute Laplacian of a full-color image by computing p p g y p g Laplacian of each component image separately

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54 Image segmentation based on color Segmentation in HSI color space

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56 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 (, ) za T [( z a ) ( za )] 1/2 [( z a ) ( z a ) ( z a ) ] /2 R R G G B B The locus of points such that D(z,a)D 0 is a solid sphere of radius D 0 A useful generalization to the distance measure C is the covariance matrix of the samples representative of the color we wish to segment T Dza (, ) [( z a) C ( z a)] 1 1/2

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59 Color edge detection (vector-valued images) For scalar images-gradientgradient 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 multiimage, CVGIP, Vol. 33, pp r,g,b unit vectors along the R, G, and B axis of RGB color space, dfi define vectors R G B u g g b x x x R G B v g g b y y y

60 Define g xx, g yy and g xy in terms of the dot product of vectors u and v as: T R R R gxx uu. u u x x x T R R R gyy vv. v v y y y T R R G G B B gxy uv. u v x y x y x y It can be shown that (DiZenzo[1986]) the direction of maximum rate of change of c(x,y) )is given by the angle 1 2g xy 1 tan 2 ( gxx gyy ) This produces 2 values 90apart, 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 F( ) ( gxx gyy ) ( gxx gyy )cos2 2gxy sin2 2 1/2

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64 Noise in color images

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67 Color image compression

68 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.

69 Show that reduces to T Dza (, ) [( z a) C ( z a)] Dza (, ) za T [( za) ( za)] 1/2 1 1/2 [( z a ) ( z a ) ( z a ) ] When C=I (identity matrix) /2 R R G G B B