人机接口与图形学双语imageformation.ppt

* Metamers:同色异构物 /条件等色 * glossed over掩盖 Tricky棘手的 Polarization偏振 * Spectral 光谱 * Retouch:润饰 * SHX=[1,a,0;0,1,0;0,0,1] Image Compositing Given images A B, we can compute C = A over B C = αAA + (1 - αA) αBB if we pre-multiply α values, this simplifies to C = A + (1 - αA) B Image Compositing This is only one possible compositing operator there are in fact 12 possible ways of combining 2 images read Foley 17.6.1 for further details Example: Image Compositing Read RGB α values from frame buffer Given RGB colors A=(0.8, 0.6, 1.0) and B=(1, 1, 1); αA= 0.5; αB= 0.2; Premultiply: A’= αAA = (0.4, 0.3, 0.5) B’= αBB = (0.2, 0.2, 0.2) C = A over B αC= FAαA + FBαB = FAA’ + FBB’ =(1)αA + (1- αA)αB = (1)A’ + (1- αA)B’ =0.5 + (0.5)0.2 = 0.6 =(0.5, 0.4, 0.6) De-premultiply: C = C’/ αC =(0.83, 0.67, 1.0) Write RGBα values back into frame buffer Image Processing Construction of an image B as a function of an image A point processing: function of corresponding pixel only example: B[x,y] = sqrt(A[x,y]) filtering: function of local neighborhood example: B[x,y] = average of neighbors of A[x,y] largely based on signal processing theory Image Processing Image processing is a key component in: retouching scanned photos (e.g., sharpening) automatic segmentation (e.g., foreground vs. background) image compression, particularly lossy schemes like JPEG and many others… Simple Point Processing Examples Invert image: f(p) = 1-p for grayscale images, maps black to white and white to black affect on RGB images is a little less obvious Simple Point Processing Examples Power law transformation: f(p) = pk brightens if k 1 identity if k =1 darkens if k 1 Image Warping Instead of modifying pixel values, map pixels to new