CS1674: Homework 3 - Programming

• image is a color image of class uint8 which you should convert to grayscale and double in your function.

• Each of x,y is an nx1 vector that denotes the x and y locations, respectively, of each of the n detected keypoints (i.e. points with "cornerness" R scores greater than a threshold who survive the non-maximum suppression). Keep in mind that x denotes the horizontal direction, hence columns of the image, and y denotes the vertical direction, hence rows, counting from the top-left of the image.
• scores is an nx1 vector that contains the R score for each detected keypoint.
• Ih,Iv are matrices with the same number of rows and columns as your input image, and store the gradients in the x (horizontal) and y (vertical) directions at each pixel.

1. [5 pts] Let's do some preprocessing. First, set some parameters for use in your functions, at the beginning of your function: set the value of k (from the "Harris Detector: Algorithm" slide) to 0.05, and use a window size of 5. Second, read in the image, and convert it to grayscale. Compute the horizontal image gradient Ih and the vertical image gradient Iv; you can reuse code to compute gradients from HW2P. Finally, initialize with zeros a matrix R of the same size as the image that will store the "cornerness" scores for each pixel.
2. [10 pts] Use a double loop to compute the cornerness score R(i, j) at each pixel i, j. This score depends on a 2x2 matrix M computed for each pixel, as shown in the slides. This matrix relies on all neighbors of i, j that are less than half_window_size away from it. Thus, the matrix M for a given pixel is a summation of window_size^2 matrices, each of size 2x2. Each of the 2x2 entries is the product of gradient image values at a particular pixel. After computing M, use the formula from class to compute the R(i, j) score for that pixel.
3. [5 pts] After computing all R(i, j) scores, it is time to threshold them in order to find which pixels correspond to keypoints. You can set the threshold for the "cornerness" score R however you like; for example, you can set it to 5 times the average R score. Alternatively, you can simply output the top n keypoints (e.g. top 1%).
4. [10 pts] Perform non-maximum suppression by removing those keypoints whose R score is not larger than all of their 8 neighbors; if a keypoint does not have 8 neighbors, remove it. The scores/x/y that you output should correspond to the final set of keypoints, after non-max suppression. Tip: Don't remove indices while looping over pixels; instead keep a vector of indices you want to remove, then set the keypoints at those indices to [].
5. [10 pts] Display the input image, and visualize the keypoints you have detected, for example by drawing circles over them. Use the scores variable and make keypoints with higher scores correspond to larger circles, e.g. plot(x(i), y(i), 'ro', 'MarkerSize', scores(i) / 1000000000); Include the visualization for three images in your submission (named vis1.png, vis2.png, vis3.png).

• extract_keypoints.m, vis1.png, vis2.png, vis3.png