Digital Image Processing

Transcription

1 Digital Image Processing 7. Color Transforms Keuyhong Cho Non-linear Color Space Reflect human eye s characters 1) Use uniform color space 2) Set distance of color space has same ratio difference of color perception in human HSI Color Space? - Classify colors H(hue), S(saturation), I(intensity) - Related Non-linear function to XYZ color space 2/42

2 HSI Color Space Express colors easy to understand of human Hue : Present color spectrum - Has information of color Saturation : Present purity - Has information of how many mixed white? Intensity : Present brightness HSI Color Space is suitable to present surface color than light source. 3/42 HSI Color Example 4/42

3 Munsell s Color Solid Defined 'object color' as the color of its surface color - Using HSI color space 5/42 Munsell s Color Solid - Hue Devide whole colors to 5 principal hues (5P, 5B, 5G, 5Y, 5R) - Colors are most easy to distinction by human s eye. 6/42

4 Munsell s Color Solid - Saturation Low Saturation means more mixed white color Saturation placed so as to have a constant difference visually Maximum saturation value depend on intensity(i) and hue(h) of color. Ex) 5R,5Y and 5YR :14 5RP :12 5BG : 8 7/42 Munsell s Color Solid - Intensity Expressed 0(Black) ~ 10(White) Weber s law ; In order to change in the organoleptic stimulus in the law that should be simulated by more than a certain percentage for the first stimulus. 8/42

5 Color Space and Transform Color Spaces RGB, CMYK, YIQ, YCbCr, HSV(HIS, HLS), CIE, etc. Why we use these various spaces? - If I want this picture to change gray-scaled image, how do I change the RGB values? What is the best way? 9/42 Coordinate Transform In space, variables presented by set of axis. - Use another axis, the specific value does not changed Can present same value using another coordinates. ex) Rectangular coordinate and Spherical coordinate in point. P Point P 1 Rectangular = (1,1,1) Spherical = ( 3, ), ) /42

6 Color Transform Both coordinates are have relationship function.,, to,,,, to,, RGB to HSI function presented angle of hue(), and saturation, intensity [( R G) ( R B)] 3 cos S 1 [min( R, G, B)] 2 1/2 [( R G) ( R B)( G B)] ( R G B) if B G 1 H I ( R G B) 360 if B G 3 *The input RGB values are in the nominal range [0.0, 1.0] 11/42 RGB Color Model & HSV Color Model 12/42

7 Color image and its components Component can be separated into each axis CMYK RGB HSI 13/42 CIE RGB Color Space (last class) The first defined quantitative links are - physical pure colors in the electromagnetic Visible spectrum - physiological perceived colors in human Color Vision Found the color matching function through the experiment in 1920s, and used this to define CIE RGB Color Space. 14/42

8 CIE XYZ Color Space (last class) Defined XYZ Color Space based on the CIE RGB color space - Obtained through a linear transformation from the RGB color space - All this has a positive value - White will be in the point - All colors that humans can see must be present in the triangle [1,0], [0,0], [0,1] After that, Calculate XYZ by this function 15/42 CIE xy Color Space Y parameter : measured of the brightness or luminance XYZ colors can be specified by the two parameters - x, y 16/42

9 Uniform Color Space The distance difference between the xy coordinate system did not match the color difference the human eye (MacAdam, 1942) New coordinate ; Difference between the visual color in proportion to the distance from the coordinate system after conversion to the color space uniformly appeared = Uniform color space 17/42 MacAdam Ellipses An ellipse that was connected to the coordinates in the xy coordinate system range to feel the same color difference. MacAdam Ellipses (Original Scale) MacAdam Ellipses (10:1 Scale) Uniform Color Space ; The ellipse to be a circle. 18/42

10 Uniform Color Space (1/5) CIE 1960 (1) CIE 1960 UCS diagram (uv coordinate) u X 4X 15Y 3Z v X 6Y 15Y 3Z nm 550nm 600nm 650nm y v x MacAdam s ellipses of equally perceptible color differences. (Ellipses are 10 times their actual length) nm u CIE 1960 uv UCS diagram 19/42 Uniform Color Space (2/5) CIE 1976 (2) CIE 1976 UCS diagram (u v coordinate) : multiply v axis 1.5x of uv coordinate nm u 4X X 15Y 3Z 550nm v 9Y X 15Y 600nm 3Z 650nm nm 550nm 600nm 650nm 0.3 v' 0.3 v nm u nm u' CIE 1960 uv UCS diagram CIE 1976 u v UCS diagram 20/42

11 Uniform Color Space (3/5) CIE 1964 (3) CIE 1964 U*V*W* diagram : 3-dimension present of uv coordinate U V W 13W 13W ( u u (v v 1/ 3 25Y n n ) ) 17 u n, v n : u, v in D65 U*V* chromatic diagram at W*=50 21/42 Uniform Color Space (4/5) CIE 1976 (4) CIE 1976 L*u*v* diagram : multiply V* scale 1.5x of U*V*W* space L 116 Y Y n L * 1/3 16 u v v* 13L ( u u ) 13L (v v ) n n Y L Y n For Y/Y n < u* Sketch of CIE 1976 L*u*v* color space 22/42 Macadam s ellipses ploted in u*v* cross section of the CIE 1976 L*u*v* uniform color space

12 Uniform Color Space (5/5) CIE 1976 (5) CIE 1976 L*a*b* diagram L a b 116f 500 f 200 f Y Y n Y Y 16 f n X X n f Y Y Z Z n n 1/ 3 f ( q) q for q f (q) q for q Sketch of CIE 1976 L*a*b* uniform color space with outer boundary generated by optimal stimuli with respected to CIE standard illuminant D65 and CIE 1964 supplementary standard observer 23/42 CIE 1976 L*a*b* Most commonly used uniform color space. Best express human visual characteristics. Most effectively express difference between the color of human eyes 24/42

13 Munsell s color in CIE coordinates xy coordinate L*u*v* coordinate 25/42 L*a*b* coordinate Pseudo-color Image Processing Add a color to gray image. Is used to emphasize the visual recognition. How to do? - Intensity slicing - Gray level to color transform 26/42

14 Pseudo-color Image Processing Intensity slicing - Specific brightness and color to assign a specific value or area f ( x, y) c if f ( x, y) k V k 27/42 Pseudo-color Image Processing 28/42

15 Pseudo-color Image Processing 29/42 Pseudo-color Image Processing 30/42

16 Pseudo-color Image Processing Gray level to color transform - Specific brightness and color to assign a specific value or area - Result to provide R, G, B image - Each color can be obtained by a variety of image according to the conversion formula and conversion method. 31/42 Pseudo-color Image Processing Gray level to color transform 32/42

17 Pseudo-color Image Processing Gray level to color transform produced image (b) in the previous example. produced image (c) in the previous example. 33/42 Full-color Image Processing Individual component based processing - Separate the color image with the individual components (ex, RGB, HSI) - Apply image processing techniques for each component - By combining the final results will be restored to the color image 34/42

18 Full-color Image Processing Vector-based processing - Processing the color image to a vector - Vector operation is performed using a process for the direct color image 35/42 Component based processing Color Complements 36/42

19 Component based processing Color Correction 37/42 Component based processing Histogram Processing 38/42

20 Component based processing Color image smoothing 39/42 Component based processing Color image smoothing 40/42

21 Component based processing Image segmentation 41/42 Component based processing Image segmentation 42/42