Definition of Image Processing Welcome to fivedotscoepsu 定义图像处理欢迎fivedotscoepsu.pptVIP

Statistical Operations Gray-level transformation Histogram equalization Multi-image operations Histogram If the number of pixels at each gray level in an image is counted (may use the following code fragment) for (row=0; rowrowmax; roww++) for (col=0; col=colmax; col++) { count[image[row,col]]++; } Histogram The array “count” can be plotted to represent a “histogram” of the image as the number of pixels at particular gray level The histogram can yield useful information about the nature of the image. An image may be too bright or too dark. Histogram Illustration Global Attenuation in Brightness To alter the brightness of an image by adding or subtracting all pixel values with a constant for (row=0; rowrowmax; roww++) for (col=0; col=colmax; col++) { image[row,col] += constant; } Thresholding Use: To remove the gray level trends in an image To make gray level more discrete To segment or split an image into distinct parts Operation: setting all gray levels below a certain level to “zero”, and above a certain level to a maximum brightness Code for Thresholding for (row=0; rowrowmax; roww++) for (col=0; col=colmax; col++) { if (image[row,col] threshold) image[row,col] = MAX; else image[row,col] = MIN; } Thresholding Errors Rarely is it possible to identify a perfect gray level break, what we want to be background pixels become foreground or vice versa Type 1: not all pixels caught that should be included Type 2: some pixels caught should not be included in the group An image before and after thresholding Bunching (Quantizing) Use: to reduce the number of different gray level in an image to segment an image to remove unwanted gray level degradation Operation: Close gray levels are combined, thus removing unwanted variations in data Bunching (Quantizing) Method 1: inspecting histogram and combining close group into single gray level Method 2: identifying a set of gray levels allowed in the f