For a long time I limited myself to one color as a form of discipline. Pablo Picasso. Color Image Processing

Transcription

1 For a long time I limited myself to one color as a form of discipline. Pablo Picasso Color Image Processing 1

2 Preview Motive - Color is a powerful descriptor that often simplifies object identification and extraction from a scene. - Human can discern thousands of color shades and intensities, compared to about only two dozen shades of gray. 2

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4 Color Fundamentals Light Light is fundamental for color vision Unless there is a source of light, there is nothing to see! What do we see? We do not see objects, but the light that has been reflected by or objects transmitted through the 4

5 Color Fundamentals The experiment of Sir Isaac Newton, in when a beam of sunlight passes through a glass prism, the emerging beam is split into a spectrum of colors 5

6 Color Fundamentals Light and EM waves Light is an electromagnetic wave If its wavelength is comprised between 400 and 700 nm (visible spectrum), the wave can be detected by the human eye and is called monochromatic light 6

7 Color Fundamentals Visible light Chromatic light span the electromagnetic spectrum (EM) from 400 to 700 nm 7

8 What is color? It is an attribute of objects (like texture, shape, smoothness, etc.) It depends on: 1) spectral characteristics of the light source(s) (e.g., sunlight) illuminating the objects (relative spectral power distribution(s) SPD) 2) spectral properties of objects (reflectance) 3) spectral characteristics of the sensors of the imaging device (e.g., the human eye or a digital camera) 8

9 Color fundamentals The color that human perceive in an object = the light reflected from the object scene Illumination source Colours Absorbed eye reflection 9

10 Color Fundamentals Basic quantities to describe the quality of light source: Radiance: Total amount of energy that flows from the light source (in W). Luminance: A measure of the amount of energy an observer perceives from the light source (in lm) Far infrared light: high radiance, but 0 luminance Brightness: A subjective descriptor that embodies the achromatic notion of intensity and is practical impossible to measure. 10

11 How human eyes sense light? 6~7M Cones are the sensors in the eye 3 principal sensing categories in eyes Red light 65%, green light 33%, and blue light 2% Absorption curves for the different cones have been determined experimentally 11

12 Primary and Secondary Colors Due to the different absorption curves of the cones, colors are seen as variable combinations of the socalled primary colors: red, green, and blue Their wavelengths were standardized by the CIE in 1931: red=700 nm, green=546.1 nm, and blue=435.8 nm The primary colors can be added to produce the secondary colors of light, magenta (R+B), cyan (G+B), and yellow (R+G) 12

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14 Primary colors of light v.s. primary colors of pigments Primary color of pigments Color that subtracts or absorbs a primary color of light and reflects or transmits the other two Color of light: R G B Color of pigments: absorb R absorb G absorb B Cyan Magenta Yellow C= 1-R, M= 1-G, Y= 1- B 14

15 Color Characteristics The characteristics generally used to distinguish one color from another are Brightness, Hue, and Saturation. Brightness: a subjective (practically unmeasurable) notion that embodies the intensity of light Hue: Represents dominant color as perceive by an observer. Saturation: Relative purity or the amount of white light mixed with a hue The pure colors are fully saturated (Red) Pink =(Red + White) which is less saturated So the degree of saturation being inversely proportional to the amount of white light added Hue and saturation taken together are called Chromaticity, and therefore, a color may be characterized by its Brightness and Chromaticity. 15

16 Tri-stimulus values: The amount of Red, Green and Blue needed to form any particular color Denoted by: X, Y and Z Z Y X X x Z Y X Y y Z Y X Z z 1 z y x Tri-chromatic coefficient: Where x,y,z : are called as Chromatic coefficient 16

17 Chromaticity Diagram 17

18 Specifying colors systematically can be achieved using the CIE chromaticity diagram. This is a special diagram which specifying any color as function of Red (x) and green(y). On this diagram the x-axis represents the proportion of red the y-axis represents the proportion of red used The proportion of blue used in a colour is calculated as: z = 1 (x + y) Green: 62% green, 25% red and 13% blue Red: 32% green, 67% red and 1% blue 18

19 Any color located on the boundary of the chromaticity chart is fully saturated i.e. if we move away from boundary the color becomes less and less saturated. The point of equal energy has equal amounts of each color and is the CIE standard for pure white Any straight line joining two points in the diagram defines all of the different colors that can be obtained by combining these two colors additively x y All possible mixture of these two colors can create all the colors which are lying on the straight line segment. 19

20 Similarly if we three points and make a triangle inside chromaticity chart then some color on the boundary or inside the triangle can be produced by various combinations of three initials colors. This means the entire color range cannot be displayed based on any three colors The triangle shows the typical color gamut produced by RGB monitors The strange shape is the gamut achieved by high quality color printers 20

21 Color Gamut produced by RGB monitors Color Gamut produced by high quality color printing device 21

22 If we take any boundary point and dray a line joining to white that gives all shades of colors that can be possible by adding white light to the fully saturated boundary color Applications of Chromaticity Diagram To specify any color Useful for color mixing Useful for high quality printing devices. 22

23 Colors in computer graphics and vision Color Models The purpose of a color model (also called color space or color system) is to facilitate the specification of colors in some standard, generally accept way. RGB (red,green,blue) : monitor, video camera. CMY(cyan,magenta,yellow),CMYK (CMY, black) model for color printing. and HSI model,which corresponds closely with the way humans describe and interpret color. 23

24 Color models Color model, color space, color system Specify colors in a standard way A coordinate system that each color is represented by a single point RGB model CYM model CYMK model HSI model Suitable for hardware or applications - match the human description 24

25 RGB color model 25

26 RGB color model In the RGB model each color appears in its primary spectral components of red, green and blue The model is based on a Cartesian coordinate system RGB values are at 3 corners Cyan magenta and yellow are at three other corners Black is at the origin White is the corner furthest from the origin Different colors are points on or inside the cube represented by RGB vectors 26

27 Pixel depth Pixel depth: the number of bits used to represent each pixel in RGB space Full-color image: 24-bit RGB color image (R, G, B) = (8 bits, 8 bits, 8 bits) The RGB Color Model If R,G, and B are represented with 8 bits (24-bit RGB image), the total number of colors is (28 )3=16,777,216 27

28 Images represented in the RGB color model consist of three component images one for each primary color When fed into a monitor these images are combined to create a composite color image 28

29 RGB is great for color generation RGB is useful for hardware implementations and is serendipitously related to the way in which the human visual system works Application Color camera Color monotors However, RGB is not a particularly intuitive way in which to describe colors 29

30 The CMY Color Model Cyan, Magenta and Yellow are the secondary colors of light CMY model is Substractive Complementary to RGB: Most devices that deposit colored pigments on paper, such as color printers and copiers, require CMY data input. B G R Y M C

31 CMYK color model CMYK K is for black Save on color inks, by using black ink preferably K = min(c,m,y) C = C-K M = M-K Y = Y-K 31

32 HSI color model Will you describe a color using its R, G, B components? Human describe a color by its hue, saturation, and brightness Hue: color attribute Saturation: purity of color (white->0, primary color- >1) Brightness: achromatic notion of intensity RGB, CMY, and the like are hardware-oriented color spaces (suited for image acquisition and display) The HSI (Hue, Saturation, Intensity) is a perceptive color space (suited for image description and interpretation) 32

33 HSI color model The HSI model uses three measures to describe colors: Hue: A color attribute that describes a pure color (pure yellow, orange or red) Saturation: Gives a measure of how much a pure color is diluted with white light Intensity: Brightness is nearly impossible to measure because it is so subjective. Instead we use intensity. Intensity is the same achromatic notion that we have seen in grey level images 33

34 HSI color model RGB -> HSI model Intensity line Colors on this triangle Have the same hue saturation 34

35 Consider if we look straight down at the RGB cube as it was arranged previously We would see a hexagonal shape with each primary color separated by 120 and secondary colors at 60 from the primaries HSI Color Model So the HSI model is composed of a vertical intensity axis and the locus of color points that lie on planes perpendicular to that axis 35

36 HSI Color Model To the right we see a hexagonal shape and an arbitrary color point The hue is determined by an angle from a reference point, usually red The saturation is the distance from the origin to the point The intensity is determined by how far up the vertical intensity axis this hexagonal plane sits (not apparent from this diagram 36

37 HSI Color Model Because the only important things are the angle and the length of the saturation vector this plane is also often represented as a circle or a triangle 37

38 HSI Model Examples 38

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40 HSI component images R,G,B Hue saturation intensity 40

41 Converting colors from RGB to HSI G B if 360 G B if H 2 1/ 2 1 )] )( ( ) [( )] ( ) [( 2 1 cos B G B R G R B R G R )],, [min( ) ( 3 1 B G R B G R S ) ( 3 1 B G R I The HSI Color Models 41

42 Converting colors from HIS to RGB RG sector : The HSI Color Models 0 H 120 B I( 1 S) R I1 ScosH cos( 60 H) G 3I ( R B) 42

43 Converting colors from HIS to RGB GB sector : The HSI Color Models H H H 240 R G I( 1 S) I1 ScosH cos( 60 H) B 3I ( R G ) 43

44 Converting colors from HIS to RGB BR sector : The HSI Color Models H G B I1 240 H 360 H 240 I( 1 S ) ScosH cos( 60 H) R 3I ( G B) 44

45 Pseudo color Image Processing Pseudo color (also called false color) image processing consists of assigning colors to grey values based on a specific criterion The principle use of pseudo color image processing is for human visualization Humans can discern between thousands of color shades and intensities, compared to only about two dozen or so shades of grey 45

46 Pseudo Color Image Processing Intensity Slicing-- Intensity slicing and color coding is one of the simplest kinds of pseudo color image processing First we consider an image as a 3D function mapping spatial coordinates to intensities (that we can consider heights) Now consider placing planes at certain levels parallel to the coordinate plane If a value is one side of such a plane it is rendered in one color, and a different color if on the other side 46

47 Intensity slicing 3-D view of intensity image Color 1 Color 2 Image plane 47

48 Intensity slicing In general intensity slicing can be summarized as: Let [0, L-1] represent the grey scale Let l 0 represent black [f(x, y) = 0] and let l L-1 represent white [f(x, y) = L-1] Suppose P planes perpendicular to the intensity axis are defined at levels l 1, l 2,, l p Assuming that 0 < P < L-1 then the P planes partition the grey scale into P + 1 intervals V 1, V 2,,V P+1 48

49 Grey level color assignments can then be made according to the relation: f (x, y) c k if f (x, y) V k where ck is the color associated with the k th intensity level V k defined by the partitioning planes at l = k 1 and l = k 49

50 Application 1 H.R. Pourreza 50

51 Application 2 Rainfall statistics 51

52 Gray level to color transformation Intensity slicing: piecewise linear transformation General Gray level to color transformation 52

53 Gray level to color transformation 53

54 Application 1 54

55 Combine several monochrome images Example: multi-spectral images 55

56 Washington D.C. R G B Near Infrared (sensitive to biomass) R+G+B near-infrared+g+b 56

57 57 B G R c c c c B G R Let c represent an arbitrary vector in RGB color space ), ( ), ( ), ( ), ( ), ( ), ( ), ( y x B y x G y x R y x c y x c y x c y x c B G R For an image of size M*N, Basic of Full Color Image Processing

58 Basic of Full Color Image Processing 58

59 Basic of Full-Color Image Processing Color Transformation Processing the components of a color image within the context of a single color model. g( x, y) T f ( x, y) r, r r 2,,, i 1,2 n si Ti 1 n,..., Color components of g Color components of f Color mapping functions 59

60 Full-Color Image Processing Color Transformation CMYK RGB Some difficulty in interpreting the HUE: Discontinuity where 0 and 360 º meet. HSI Hue is undefined for a saturation 0 60

61 Full-Color Image Processing Color Transformation: Color Complement 61

62 Full-Color Image Processing Color Transformation: Color Slicing Motive: Highlighting a specific range of colors in an image Basic Idea: Display the color of interest so that they stand out from background Use the region defined by the colors as a mask for further processing W 0.5 if rj aj s i 2, i 1,2,..., n any1 jn ri otherwise 62

63 63 Full-Color Image Processing Color Transformation: Color Slicing n i otherwise r W a r if s i n j any j j i 1,2,...,, Colors of interest are enclosed by cube (or hypercube for n>3) 2. Colors of interest are enclosed by Sphere n i otherwise r R a r if s i n j j j i 1,2,...,, ) (

64 Full-Color Image Processing Color Transformation: Color Slicing Cube Sphere 64