Digital Color Processing Hamid Sarve

Transcription

1 Lecture 6 in Computerized Image Analysis Hamid Sarve hamid@cb.uu.se Reading instructions: Chapter 6 The slides 1

2 Electromagnetic Radiation Visible light (for humans) is electromagnetic radiation with wavelengths in the approximate interval of nm Gamma Xrays UV IR μwaves TV Radio nm 10 nm 0.01 m VISIBLE LIGHT 400 nm 500 nm 600 nm 700 nm 2 2

3 Light Properties Illumination Achromatic light - White or uncolored light that contains all visual wavelengths in a complete mix. Chromatic light - Colored light. Monochromatic light - A single wavelength, e.g., a laser. Reflection No color that we see consists of only one wavelength The dominating wavelength reflected by an object decides the color tone or hue. If many wavelengths are reflected in equal amounts, an object appears gray. 3 3

4 The Human Eye Rods and Cones (stavar och tappar) rods only rods cone mostly cones only rods optical nerves 4 4

5 The Rods Approx. 100 million rod cells per eye Light-sensitive receptors Used for night-vision Not used for color-vision! Rods have a slower response time compared to cones, i.e. the frequency of its temporal sampling is lower 5 5

6 The Cones Approx 5 million cone cells per eye Three types: S, peak at 445 nm (2%) M, peak at 535 nm (33%) L, peak at 575 nm (65%) Most sensitive to green light! Responsivity Primary colors 400 nm 500 nm 600 nm 700 nm 6 6

7 Mantis shrimp bönsyrseräka 12 type of color receptors! 7 7

8 Remark about our Vision The appearance of an object s intensity depends on the surroundings; the sensation is relative and not absolute 8 8

9 Trisimulus Values CIE, International Commission on Illumination 2 /10 standard observer X = s( λ) r( λ) x( λ) dλ Y = s( λ) r( λ) y( λ) dλ Z = s( λ) r( λ) z( λ) dλ standardized light source s( λ) x reflectance of the object r( λ) x CIE 1931 standard observer z y x CIE XYZ values = X=14.27 Y=14.31 Z= nm 700nm 400nm 700nm 400nm 700nm 9 9

10 CIE 1931 Color Space The projection of X + Y + Z = 1 creates CIE xy chromaticity diagram CIE standard white when x = y = z Not possible to produce all colors by mixing three primary colors. Gamut: subset of colors which can be accurately represented in a given circumstance 10 10

11 RGB: Red, Green, Blue Additative mix R + G + B = White, where R + G = Y, etc. %RGB in matlab rgbimg = imread( colorful.jpg ); Rchannel = rgbimg (:,:,1); %uint8 Gchannel = rgbimg (:,:,2); %uint8 Bchannel = rgbimg (:,:,3); %uint8 The RGB cube 11 11

12 Additative Mix R channel G channel B channel RGB: 12 12

13 CMYK [C M Y] = 1 [R G B] Subtractive mix C + M + Y = black where C + M = B, etc Common in printing The CMY cube 13 13

14 RGB - Pros & Cons + Mimicing the human color perception Easy and straightforward Suited for hardware implemenation - Not practical for human description of colors No decoupler of intensity 14 14

15 HSV (HSI) Related to how humans perceive color Intensity is decoupled Channels: Hue: dominant wavelength (in degrees!) Saturation: colorfulness (intensity of the color) Intensity/Value brightness % HSV in matlab rgbimg = imread( colorful.jpg ); hsvimg = rgb2hsv(rgbimg); Hchannel Schannel Vchannel = hsvimg (:,:,1); %dble = hsvimg (:,:,2); %dble = hsvimg (:,:,3); %dble 15 15

16 RGB to HSV θ H = 360 θ if if B B > G G θ = cos for s=0 not defined! [( R G ) + ( R B) ] 2 ( R G ) + ( R B)( G B) S = 1 3 [ min ( R, G, B )] R + G + B V or V = = ( R 3 + G R G + B ) B There are also other definitions! 16 16

17 HSV H channel S channel V channel HSV: 17 17

18 Usage of HSV Intensity is decoupled That s often where we want to operate Original image RGB image histogram equalized for each channel individually HSV image histogram equalized for the V-channel only 18 18

19 HSV usage, cont Masking out a region with similiar color Find the pixels in the range of the desired color in the Hue-channel Set all other pixels to 0 in the Saturation-channel %imghsv : HSV-transformed image %masked H-value: and mask = (imghsv(:,:,1) < 0.1) + (imghsv(:,:,1)> 0.9); S = imghsv(:,:,2).* mask; imghsv_r (:,:,3) = imghsv(:,:,3); imghsv_r (:,:,2) = S; imghsv_r (:,:,1) = imghsv(:,:,1); 19 19

20 HSV usage, cont Another example Find the pixels in the range of the desired color in the Hue-channel Set all other pixels to 0 in the Saturation-channel %imghsv : HSV-transformed image %H-value of the tree : mask = (imghsv(:,:,1) < 0.15) + (imghsv(:,:,1)> 0.27); S = imghsv(:,:,2).* (1-mask); imghsv_g (:,:,3) = imghsv(:,:,3); imghsv_g (:,:,2) = S; imghsv_g (:,:,1) = imghsv(:,:,1); 20 20

21 HSV- Pros & Cons + Practical for human description of colors Intensity decoupled - Difficult transformation (singularities) Difficult to display Furthermore: Not device independent (the latter also applies to RGB) 21 21

22 CIE L*a*b* Device independent Perceptually uniform Intensity decoupled studies indicate that L*a*b* separates information better than other color spaces For transformation, see in the course book Perceptual equal distances 22 22

23 L*a*b* L* channel a* channel b* channel L*a*b*: 23 23

24 Other Color Spaces YCbCr similar to L*a*b* Used for JPEG Uses the fact that the human eye is more sensitive to variations in lightness than variations in hue and saturation, and more bandwidth (bits) is used for Y. Similar color spaces: YIQ / YUV used for TV 24 24

25 Pseudo Coloring The eye can distinguish between only about different shades of gray But about 100,000 10,000,000 colors. Useful to display gray scale images using color to visualize the information better important to include a color scale in the images to understand what the colors illustrate. It seems rather cumbersome to measure the number of distinguishable colors by humans. Follow the discussion at:

26 Example of Pseudo-Coloring in PET Intensity slicing Each intensity is assigned a color 26 26

27 Another Example of Pseudo-Coloring Lake Mälaren, 1997 chlorophyll suspended inorganic particulate material coloured dissolved organic matter 27 27

28 Noise in Color Images Noise less visually noticeable in color images μ=0, v=0.01 Gaussian Noise Grayscale image (on V only) RGB (each channel distored individually) 28 28

29 Noise in the HSV-space Noise most visible in the H- and S-channel H S V 29 29

30 Smoothing Neighborhood averaging can be carried on a per-color-plane basis - or on the intensity only Different results however! See below: Original HSV (on V only) RGB (each channel filtered individually) 7x7 Gaussian 30 30

31 Look out for the H-channel You might end up with color artefacts if H-channel is filtered Remember: H-channel consists of angles H-channel blurred with a 3x3 Gaussian 31 31

32 Edge Detection As in smoothing: per-color-plane / intensity channel Original HSV (on V only) RGB (each channel filtered individually) Laplace 32 32

33 Segmentation based on Color In RGB-space: Look at the euclidean distance: D ( z, a ) = z a where z is an arbitary point in the RGB space, a is the RGB vector = [( z R a R ) 2 + ( z ( z If D(z,a) < Dt: Pixel belongs to the object (where Dt is the tolerated difference) R G a G ) 2 + B a B ) 2 ] Color-based segmentation in HSV-space: color information is found in the Hue- & Saturation-channel Dt:radius of the spehere G Inside the sphere: the object z Outside the sphere: background 33 33

34 Segmentation based on Color Example: Original Segmented sky 34 34

35 Compression of Color Images Our vision is less sensitive to fine color details than to fine brightness details Concentrate the compression on the chroma (color) information 35 35

36 Multispectral Imaging A more general description of an image: f(x,y,z,w,t) x,y,z 3 spatial dimensions t time -temporal dimension w wavelength, spectral dimension Images in sampled in different wavelengths Not only in the visible spectra Remember the shrimp? 36 36

37 Multispectral imaging cont. Gamma Xrays UV IR μwaves TV Radio nm 10 nm 500 nm 10 um 0.01 m Used in (among others) Remote sensing Astronomy Thermography 37 37

38 Assignment 3 Landsat Satellite Image: 7 Bands, um from the visual spectra to IR Your data: 512 x 512 x