YUV _YC C _ Color Model by bestt571

VIEWS: 31 PAGES: 9

More Info
									        July 13, 2007 (Class 4: 1/2007)

ENE 461 Introduction to Digital Image Processing
         Werapon Chiracharit, Ph.D., ENE, KMUTT




     YUV (YCbCr) Color Model
                 Y=R+G+B

                                 U=B-Y
                                                  V=R-Y




Y is the lumimance (brightness)
U is the chrominance blue-luma difference
V is the chrominance red-luma difference




                                                          1
             Otsu’s Thresholding
•   Probability distribution from histogram, Pi=ni/N
•   For any threshold = k for bimodal assumption
•   Frequency, ω1=Σ0→kPi and ω2=Σk+1→L-1Pi
•   Mean, µ1=Σ0→kiPi/ω1 and µ2=Σk+1→L-1iPi/ω2
    Mean total, µt=ω1µ1+ω2µ2
•   Variance between classes, δ2b=ω1ω2(µ1- µ1)2
•   Variance total, δ2t= Σ0→L-1(i- µt)2Pi
•   Criterion function, η=δ2b/ δ2t
•   Choose threshold k that MAX η




       Line – 1st Order Polynomial
    The line from 2 points (x1,y1) and (x2,y2)
                  Ax + By + C = 0
    Known x1, y1, x2, y2
    Unknown A, B, C
       Y
       y2                            (x2,y2)

       y1             (x1,y1)

        0        x1             x2             X




                                                       2
           Pseudo-Inverse Matrix
                         y = -(A/B)x – (C/B)
                        y1 = -(A/B)x1 – (C/B)
                        y2 = -(A/B)x2 – (C/B)

                   y1     = -x1 -1        A/B
                   y2       -x2 -1        C/B
                        2×1          2×2        2×1
                                     -1
                   A/B = -x1 -1            y1
                   C/B   -x2 -1            y2
                         2×1         2×2        2×1




               Slope – 1st Derivative



Number                         +                 -
of Pixel

               +          -


           0                                         255 Intensity
                               HISTOGRAM




                                                                     3
                            Labeled Pixels
For X = 1:Height
 For Y = 1:Weight
     I(X,Y)
                                      y-1                  y     y+1
                                        Up                         Up
                               x-1      Left            Up        Right
                                     (x-1,y-1)        (x-1,y)   (x-1,y+1)

                                x       Left          Here        Right
    1       2       3                 (x,y-1)         (x,y)      (x,y+1)
                                       Down                       Down
    4                   5               Left           Down       Right
                               x+1   (x+1,y-1)        (x+1,y)   (x+1,y+1)
        6       8
            7




                              Cropping
                                      Y               y1                   y2
0                                                x1




                                                x2

                                                       I(x1:x2,y1:y2)




X




                                                                                4
      Scale-Down Interpolation
               I(x′) = λ I(x1) + (1- λ) I(x2)

    Intensity I(x1,y1)        Intensity                 I(x2)
                0 y
                    1
      x1
                                        I(x1)
      Pixel(x1,y1)           Y
X                                               λ    1-λ
                                   0      x1                x2   X




             Image Interpolation

     X,Y                 I(X,Y′)                    X,Y+1




                          X′,Y′        I(X′,Y′)



    X+1,Y                I(X+1,Y′)              X+1,Y+1




                                                                     5
Scale-Up Interleave and Interpolation

     X,Y             0             X,Y+1




      0              0               0



    X+1,Y            0             X+1,Y+1




                Rotation
                                             Y′
            Y
                               Y




X
                           X         θ
                X′




                                                  6
                  Rotated Axis
              X′ = cosθ -sinθ X
              Y′   sinθ cosθ Y
                   Y




                                        X
                       0




                       Scaling              1.2y


                           y



       0.7y
                                 1.2x
0.7x




              x




                                                   7
  MATLAB Toolbox Functions
• imrotate(I,θ-degree) => counter-clockwise
• imresize(I,M-times) => M=[0,1] scale down
                      => M>1 scale up
  imresize(I,[height,width]) => fixed size
• zeroint(I)




         No Assignment ☺




       When you hear, you forget.
       When you see, you remember.
       When you do, you Understand.




                                              8
               Exercise
• By using no MATLAB scaling function,
  rotate your image (clockwise and
  anti-clockwise)
• Resize your image (smaller scaling and
  enlargement)

              HAVE FUN ☺




                                           9

								
To top