Image segmentation

Require Format • a pdf file with the source code (excluding libraries), the resulting images and a brief explanation of the implementation. • the code. • the output images. Problem 1: k-means Segmentation. Apply k-means segmentation on [login to view URL] with k=10. The distance function should only consider the RGB color channels and ignore pixel coordinates. Randomly pick 10 RGB triplets from the existing pixels as initial seeds and run to convergence. After k-means has converged, represent each cluster with the average RGB value of its members, creating an image as figure 1. Problem 2: SLIC. Apply a variant of the SLIC algorithm to wt [login to view URL], by implementing the following steps: 1. Divide the image in blocks of 50×50 pixels and initialize a centroid at the center of each block. 2. Compute the magnitude of the gradient in each of the RGB channels and use the square root of the sum of squares of the three magnitudes as the combined gradient magnitude. Move the centroids to the position with the smallest gradient magnitude in 3×3 windows centered on the initial centroids. 3. Apply k-means in the 5D space of x, y, R, G, B. Use the Euclidean distance in this space, but divide x and y by 2. 4. After convergence, display the output image as figure 2: color pixels that touch two different clusters black and the remaining pixels by the average RGB value of their cluster. Requirements and notes. • Use matlab or java • You are allowed to use image reading and writing functions, as well as plotting functions, but you are not allowed to use filtering, edge detection or other image processing functions. You can convert the images to a different format for reading them. • The complexity of k-means is linear with respect to the number of pixels and k. Start developing on small images with small values of k.