6 Color Image Processing

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1 6 Color Image Processing Angela Chih-Wei Tang ( 唐之瑋 ) Department of Communication Engineering National Central University JhongLi, Taiwan 2009 Fall

2 Outline Color fundamentals Color models Pseudocolor image processing Color transformations Color segmentation C.E., NCU, Taiwan Angela Chih-Wei Tang,

3 Fundamentals Two major areas full-color image processing the images in question typically are acquired with a full-color sensor, such as color TV camera or color scanner pseudo color image processing assign a color to a particular monochrome intensity or range of intensities Color spectrum: violet, blue, green, yellow, orange & red When viewed in full color, each color in the spectrum blends smoothly into the next The colors perceived in an object are determined by the nature of the light reflected from the object C.E., NCU, Taiwan Angela Chih-Wei Tang,

4 Color Spectrum C.E., NCU, Taiwan Angela Chih-Wei Tang,

5 Wavelengths A body that reflects light that is balanced in all visible wavelengths appears white to the observer A body that favors reflectance in a limited range of the visible spectrum exhibits some shades of color C.E., NCU, Taiwan Angela Chih-Wei Tang,

6 Achromatic Light & Chromatic Light Achromatic: the only attribute is intensity gray level: a scalar measure of intensity Chromatic radiance total amount of energy that flows from the light source Watts (W) Luminance the amount of energy an observer perceives from a light source lm Brightness a subjective descriptor that is practically impossible to measure one of the key factors in describing color sensation C.E., NCU, Taiwan Angela Chih-Wei Tang,

7 Cones 65%: are sensitive to red light 33%: are sensitive to green light 2%: are sensitive to blue Due to these absorption characteristics of the human eyes, colors are seen as variable combinations of the primary colors (R, G, B) C.E., NCU, Taiwan 7

8 Primary Colors of Light CIE (1965) blue=435.8 nm green=546.1 nm Red=700 nm Primary: when mixed in various intensity proportions can produce all visible colors (WRONG!) Secondary colors of light magenta (R+B) cyan (G+B) yellow (R+G) White: mix 3 primaries C.E., NCU, Taiwan Angela Chih-Wei Tang,

9 Primary Colors of Pigments Primary colors of pigments subtract/absorb a primary color of light and reflects or transmits the other two magenta cyan Yellow Black: a proper combination of the 3 pigment primaries C.E., NCU, Taiwan 9

10 Brightness, Hue & Saturation The characteristics generally used to distinguish one color from another Brightness embodies the achromatic notion of intensity Hue attribute associated with the dominant wavelength in a mixture of light waves dominant color as perceived by an observer e.g., red, orange or yellow Saturation: the relative purity or the amount of white light mixed with a hue less saturated: e.g., pink, lavender C.E., NCU, Taiwan Angela Chih-Wei Tang,

11 Trichromatic Coefficients Chromaticity: hue + saturation a color may be characterized by its brightness & chromaticity A color is specified by its trichromatic coefficients Tristimulus: the amounts of red (X), green (Y) & blue (Z) needed to form any particular color x = X X + Y + Z y = X Y + Y + Z z = X Z + Y + Z C.E., NCU, Taiwan Angela Chih-Wei Tang,

12 CIE Chromaticity Diagram Color composition is a function of x (R) & y (G) z(blue)=1-(x+y) The pure colors are indicated around boundary Any point within the diagram: some mixture of spectrum colors C.E., NCU, Taiwan Angela Chih-Wei Tang,

13 Typical Color Gamut of Color Monitors (Triangle) & Color Printing Devices (Irregular Region) C.E., NCU, Taiwan 13

14 Outline Color fundamentals Color models Pseudocolor image processing Color transformations Color segmentation C.E., NCU, Taiwan Angela Chih-Wei Tang,

15 Color Models Color model/color space/color system: a specification of a coordinate system & a subspace within that system where each color is represented by a single point RGB: the hardware-oriented models most commonly used in practice for color monitor & a broad class of color video cameras CMY & CMYK: color printing HIS: corresponds closely with the way humans describe & interpret color C.E., NCU, Taiwan Angela Chih-Wei Tang,

16 The RGB Color Model (1/2) The gray scale (points of equal RGB values) extends from black to white along the line joining these two points The different colors are defined by vectors extending from the origin Pixel depth: the number of bits used to represent each pixel in RGB space Full-color image: 24-bit RGB color image C.E., NCU, Taiwan Angela Chih-Wei Tang,

17 The RGB Color Cube C.E., NCU, Taiwan Angela Chih-Wei Tang,

18 RGB 24-Bit Color Cube C.E., NCU, Taiwan Angela Chih-Wei Tang,

19 The RGB Color Model (2/2) There are numerous applications in which it simply makes no sense to use more than a few hundred, and sometimes fewer, colors e.g., pseudocolor image processing Many systems in use today are limited to 256 colors 216 colors are common to most systems safe RGB colors/all-systems-safe colors 0, 51, 102, 153, 204, or 255 C.E., NCU, Taiwan Angela Chih-Wei Tang,

20 The 216 Safe RGB Colors & All the Grays in the 256-Color RGB System 20

21 The RGB Safe-Color Cube C.E., NCU, Taiwan Angela Chih-Wei Tang,

22 The CMY & CMYK Color Models Most devices that deposit colored pigments on paper (e.g, color printers & copiers) require CMY data input or perform an RGB to CMY conversion internally C 1 R M = 1 G Y 1 B When a surface coated with cyan pigment is illuminated with white light, no red light is reflected from the surface Equal amounts of the pigment primaries, cyan, magenta, and yellow should produce black a muddy-looking black CMYK: a fourth color, black (pure black), is added C.E., NCU, Taiwan Angela Chih-Wei Tang,

23 The HIS Color Model (1/2) RGB is ideal for image color generation (as in image capture by a color camera or image display in a monitor screen), but its use for color description is much more limited when humans view a color object, we describe it by its hue, saturation & brightness HIS (hue, saturation, intensity) color model: decouples the intensity component from the colorcarrying information (hue & saturation) in a color image C.E., NCU, Taiwan Angela Chih-Wei Tang,

24 Conceptual Relationships between the RGB & HIS Color Models The intensity component of any color point: the intersection of the plane with the intensity axis and containing the color point C.E., NCU, Taiwan Angela Chih-Wei Tang,

25 The HIS Color Model (2/2) The saturation of a color increases as a function of distance from the intensity axis the saturation of points on the intensity axis: 0 Hue: determined by an angle from some reference point usually (but not always) an angle of 0 degree from the red axis designates 0 hue, and increases counterclockwise from there all points contained in the plane segment defined by the intensity axis and the boundaries of the cube have the same hue Origin: the intersection of the color plane with the vertical intensity axis C.E., NCU, Taiwan Angela Chih-Wei Tang,

26 Hue & Saturation in the HIS Color Model 26

27 The HIS Color Model based on Triangular & Circular Color Planes C.E., NCU, Taiwan 27

28 Converting Colors from RGB to HSI Assumptions the RGB values have been normalized to the range [0,1] the angle is measured with respect to the red axis of the HSI space H θ = S θ if B G = 360 θ if B > G cos 1 [( R G) + ( R B)]/ 2 2 [( R G) + ( R B)( G B)] 3 = 1 [min( R, G, B)] ( R + G + B) I = ( R + G + B) / 3 C.E., NCU, Taiwan 28 1/ 2

29 HIS Components of the RGB 24-Bit Color Cube C.E., NCU, Taiwan Angela Chih-Wei Tang,

30 Outline Color fundamentals Color models Pseudocolor image processing Color transformations Color segmentation C.E., NCU, Taiwan Angela Chih-Wei Tang,

31 Pseudocolor (False Color) Image Processing Assign colors to gray values based on a specified criterion The principal use of pseudocolor is for human visualization & interpretation of gray-scale events in an image or sequence of images Humans can discern thousands of color shades & intensities, compared to only two dozen or so shades of gray C.E., NCU, Taiwan Angela Chih-Wei Tang,

32 Intensity (Density) Slicing C.E., NCU, Taiwan Angela Chih-Wei Tang,

33 An Example of Intensity Slicing C.E., NCU, Taiwan Angela Chih-Wei Tang,

34 Use of Color to Highlight Rainfall Levels 34

35 Gray Level to Color Transformations C.E., NCU, Taiwan Angela Chih-Wei Tang,

36 Use of Pseudocolor for Highlighting Explosives Contained in Luggage 36

37 Transformation Function Used to Obtain the Previous Images C.E., NCU, Taiwan 37

38 Color Coding of Multispectral Images 38

39 Outline Color fundamentals Color models Pseudocolor image processing Color transformations Color segmentation C.E., NCU, Taiwan Angela Chih-Wei Tang,

40 A Full-Color Image & Its Various Color-Space Components C.E., NCU, Taiwan 40

41 Color Transformations To modify the intensity of an image g ( x, y) = kf ( x, y) In the HIS color space s = r, s = r, s = kr , In the RGB color space 3 3 s i = kr i i =1,2,3 In the CMY space s i = kr + ( 1-k ) i = 1,2,3 i C.E., NCU, Taiwan Angela Chih-Wei Tang,

42 Adjusting the Intensity Color Transformations 42

43 Color Complements The hues directly opposite one another on the color circle They are analogous to the gray-scale negatives Enhance detail that is embedded in dark regions (dominant in size) of a color image C.E., NCU, Taiwan 43

44 Color Complement Transformations 44

45 Tone Corrections Transformations for modifying image tones normally are selected interactively In the RGB & CMY(K) spaces: map all color components with the same transformation Function C.E., NCU, Taiwan In the HIS color space45 only modify the intensity 45

46 Color Balancing After the tonal characteristics of an image has been properly established, any color imbalances can be addressed Accurate visual assessments are possible white areas skin tones C.E., NCU, Taiwan 46

47 Color Image Smoothing The average of two pixels of differing colors is a mixture of the two colors, not either of the original colors = xy S y x S y x y x R K y x c K y x c ), ( ), ( ), ( 1 ), ( 1 ), ( C.E., NCU, Taiwan Angela Chih-Wei Tang, = xy xy xy S y x S y x S y x y x B K y x G K K y x c ), ( ), ( ), ( ), ( 1 ), ( 1 ), (

48 Original RGB and HIS Images for Fig

49 Color Image Sharpening = ), ( ), ( ), ( )], ( [ y x B y x G y x R y x c The Laplacian of a vector is defined as a vector whose components are equal to the Laplacian of the individual scalar components of the input C.E., NCU, Taiwan Angela Chih-Wei Tang, vector

50 Histogram Processing Spread the color intensities uniformly, leaving the colors themselves (e.g., hues) Unchanged It s unwise to Histogram equalize the components of a color image independently 50

51 Outline Color fundamentals Color models Pseudocolor image processing Color transformations Color segmentation C.E., NCU, Taiwan Angela Chih-Wei Tang,

52 Color Edge Detection (1/2) The value of the gradient at point [(M+1)/2, (M+1)/2]? C.E., NCU, Taiwan Angela Chih-Wei Tang,

53 Color Edge Detection (2/2) The gradient discussed in the case of gray-level images are not defined for vector quantities using the result to form a color image will lead to erroneous results If the problem is just detecting edges, then the individual-component approach usually yields acceptable results If accuracy is an issue, a new definition of the gradient applicable to vector quantities is required C.E., NCU, Taiwan Angela Chih-Wei Tang,

54 Extend the Concept of a Gradient to Vector Functions (Di Zenzo [1986]) (1/2) The value of the rate of change at (x,y), in the direction of is F( θ) θ 1 = [( g 2 xx + g yy ) + ( g xx g yy )cos 2θ + 2g xy sin 2θ] 1/ 2 where θ = tan ( g 2g xx xy g yy ) C.E., NCU, Taiwan Angela Chih-Wei Tang,

55 Extend the Concept of a Gradient to Vector Functions (Di Zenzo [1986]) (2/2) Let r, g, and b be unit vectors along the R, G, and B axis of RGB color space b g r u x B x G x R + + = b g r v y B y G y R + + = Let u u u u x B x G x R g T xx + + = = = v v v v y B y G y R g T xx + + = = = y B x B y G x G y R x R g T xy + + = = = v u v u 55 C.E., NCU, Taiwan 55

56 Edge Detection in Vector Space C.E., NCU, Taiwan 56

57 Component Gradient Images of the Color Image in Fig C.E., NCU, Taiwan Angela Chih-Wei Tang,

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