Assignment 4 Image Segmentation using K-means




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Introduction to Computer Vision (ECSE 415)

Please submit your assignment solutions electronically via the myCourses
assignment dropbox. Attempt all parts of this assignment. The assignment will
be graded out of total of 26 points.
Submission Instructions
1. Prepare and submit a single Google Colab notebook containing answers
to all three questions.
2. Comment your code appropriately.
3. Do not submit input/output images. The output images should be displayed in the notebook. Assume the input image folders are kept in a
same directory as the codes.
4. Make sure that the submitted code is running without error. Add a
README file if required.
5. Answers to reasoning questions should be comprehensive but concise.
6. Submissions that do not follow the format will be penalized 10%.
1 Image Segmentation using K-means
Implement K-means algorithm using only the numpy library. You can use
opencv and matplotlib libraries only to read and display images. Apply Kmeans to the images ‘home’ and ‘flower’ shown in Figure 1. Try K=2 and K=3.
Run the algorithm for 10 iterations and display the resulting segmented images
in each case. (10 points)
(a) (b)
Figure 1: Segment above images using K-means algorithm.
(a) (b) (c)
Figure 2: A pair of stereo images (a) left image (b) right image (c) disparity
map (expected output).
2 Disparity
In this section, we will compute disparity map D from a pair of stereo images
captured using parallel cameras. The images are shown in Figure 2(a) and 2(b).
We will solve correspondence problem with the window search algorithm. Refer
to slides 58-59 in Lecture 18 – Stereo Vision. Instead of searching for a matching
window on the entire scanline, we will restrict the search on a small region on
the scanline.
1. Extract a 5 × 5 window centered at each pixel-location (i, j)L in the left
image. Let’s call these windows reference windows. (2 points)
2. For each reference window in the left image do the following.
(a) On the right scanline, create a search region bounded by pixel-locations
(i, j−47)R and (i, j)R. Extract 5×5 windows centered at every pixellocation in this search region. (2 points)
(b) For few boarder pixel-locations either the reference window or the
search region lie outside the boundary of the image. Set disparity
(a) (b)
Figure 3: Input frames for optical flow computation. (a) frame1 (b) frame2.
D(i, j) = 48 for these pixel-locations. For the remaining locations do
the following. (2 points)
(c) Compute sum-of-square-difference(SSD) between the windows in the
search region and the reference window. (2 points)
(d) Find a location (i
, j0
)R with minimum SSD and compute disparity
D(i, j) = jL − j
R. (Note that 0 ≤ D(i, j) ≤ 47 as the search region
contains 48 pixel-locations.) (1 point)
3. Display the final disparity map D with the cmap argument of plt.imshow
set to ‘gray r’. The expected output is shown in Figure 2(c). (1 point)
3 Optical Flow
In this section, we will observe the effect of the window-size on the prediction
accuracy of optical flow. The input frames are shown in Figure 3(a-b) and
the ground-truth flow in given in ‘flow10.npz’ file. Read ground truth flow as
follows: gt = np.load(‘flow10.npz’)[‘flow’]
1. Use calcOpticalFlowFarneback from OpenCV to compute optical flow
between the input frames with the arguments set as follows. (2 points)
• flow=None, pyr scale=0.5, levels=3, iterations=3, poly n=5,
poly sigma=1.2 and flags=0.
• Very winsize from 5 to 21 in the steps of 2.
2. For each setting of winsize, measure mean squared error(MSE) between
estimated optical flow and the ground truth optical flow. Plot MSE (yaxis) vs winsize (x-axis). (2 points)
3. Do you observe any trend in the plot above? Does the error increase or
decrease with increasing window-size? Explain the effect of window-size
on the prediction error. (2 points)