COMP 4102A: Assignment 2
1 (20 points) Harris Corner detector
The goal of this assignment is to use the routines in OpenCV to demonstrate the operations of the Harris
Corner detector. The program should do the following:
1. Open the image box-in-scene.jpg and show the image in a window.
2. Compute the minimum eigenvalue of that image. Hint: The easiest way is to use the routine cv-
CornerMinEigenVal.
3. Threshold the minimum eigenvalue, and draw the pixels that pass this threshold test in white, and
the rest of the pixels in black. The actual threshold should be set by a slider which is on top of the
window. (Try a threshold around the value of 0.01 for the minimum eigenvalue which would gives
good results as the middle value of the slider.)
4. Take the pixels that pass this threshold test and use a non-maxima suppression algorithm to thin
out the potential corners. Draw the corners that pass the non-maxima suppression test in another
window. These are the final corners. You can show these corners with small circles or cross.
As you vary the minimum eigenvalue threshold you will see that the number of detected corners changes.
The code in https://docs.opencv.org/3.4/da/d6a/tutorial_trackbar.html is an example of how to
use a slider in HighGUI.
2 (40 points) Image stitching
The goal of this question is to create a program that take as input two images that are related by a rotation
homography; a right (uttower-right.jpg), left (large2-uttower-left.jpg) and creates a single panoramic image
(same size as large2-uttower-left.jpg) as output. This is done by warping the right “into” the left image.
I have made the left image big enough to hold both the warped right and the original left image. I have
given you a program called akaze-match-template.cpp which takes these two images and computes a set of
features that you can use to compute the homography between them.
To actually compute the homography you use the routine findhomography(, , RANSAC) and then you
use warperspective routine with the computed homography to warp the right image into an image of the
same size as the left image. In other words you warp the right image into the large left image, and after
that you paste (essentially an OR operation) the warped right image into the large left image. You should
output two images; warped, which is the warped version of the right image, and merged which is the
warped version of the right image combined with the large left image. I have included two images called
warped and merged which show you how they should look like. Notice that the final merged image has
some anomalies because of the OR operation. In real mosaicking programs you do not see these anomalies.
Write down a short (one paragraph) description of how you would get rid of these visible anomalies and
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include it in your upload. In other words, I want to know what you would do in place of the OR operation
to reduce these anomalies. The answer is simple.
Note: The helper code is for you to start the assignment, the rest of the code should be written by you.
Adapt the version of the OpenCV used in the example code with the library you have downloaded (You
may not need any changes if it the function has not changed with new versions). Use codes as your guide.
Make sure you are linking the libraries.
Submission
Include your codes for Harris corner detector and Image stitching separately in two folders. Submit a
zipped folder contains both folders. For Harris corner detector, include snapshot of the slider, images
before and after applying non-maxima suppression and final result with detected corners. For the
second program, show the matching points separately on each image, the wrapped image of the right
image, and final attached left and right images after applying the homography. Please submit through
Brightspace. You are expected to work on the assignment individually.
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