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McGill University *
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Course
415
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Computer Science
Date
Jan 9, 2024
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Introduction to Computer Vision (ECSE 415)
Assignment 2: Image Matching and Face Detection
DEADLINE: March 6, 11:59 PM
Please submit your assignment solutions electronically via the
myCourses
assignment dropbox. The
submission should include a single Jupyter notebook. More details on the format of the submission
can be found below. Submissions that do not follow the format will be penalized 10%. Attempt
all parts of this assignment. The assignment will be graded out of a total of
100 points
. There
are
50 points
for accurate analysis and description,
40 points
for bug-free and clean code, and
10
points
concerning the appropriate structure in writing your report with citations and references.
Each assignment will be graded according to defined rubrics that will be visible to students. Check
out MyCourses, the "Rubrics" option on the navigation bar. You can use
OpenCV
,
Scikit-Image
,
Numpy
,
matplotlib
,
sklearn
library functions for all parts of the assignment except those stated
otherwise. Students are expected to write their own code. (Academic integrity guidelines can be
found
here
). Assignments received up to 24 hours late will be penalized by 30%. Assignments
received more than 24 hours late will not be graded.
Submission Instructions
1. Submit a single Jupyter notebook consisting of the solution of the entire assignment.
2. Comment your code appropriately.
3.
Give references for all codes which are not written by you. (Ex. the code is taken from an
online source or from tutorials)
4. Do not forget to run
Markdown
(’Text’) cells.
5.
Do not submit input/output images. Output images should be displayed in the Jupyter
notebook itself.
6.
Make sure that the submitted code is running without error. Add a
README
file if required.
7.
If external libraries were used in your code please specify their name and version in the
README
file.
8.
We are expecting you to make a path variable at the beginning of your codebase. This should
point to your working local (or google drive) folder.
Ex
. If you are reading an image in the following format:
img = cv2.imread (
’/content/drive/MyDrive/Assignment1/images/shapes.png’
)
Then you should convert it into the following:
path =
’/content/drive/MyDrive/Assignment1/images/’
img = cv2.imread(path +
’shapes.png’
)
Your path variable should be defined at the top of your Jupyter notebook. While grading, we
are expecting that we just have to change the path variable once and it will allow us to run
your solution smoothly. Specify, your path variable in the
README
file.
9. Answers to reasoning questions should be comprehensive but concise.
1
Image Classification using HoG (10 points)
Supposing that a company hires you to build a logo classification system that can automatically
identify logos of different food brands. The company provides you with some images of different
logos as training data, and you are asked to test your classification system on test images. You recall
this can be solved using Histogram of Gradient (HoG) features.
1.1
Build a classifier with training images
For this task, you are given a set of training images: five ‘Kraft’ logos and five ‘McDonald’s’ logos.
These can be found here: ‘Q1/training’ folder. Examples are found in Figures 1 and 2.
Figure 1: Example of Kraft logo
Figure 2: Example of McDonald’s logo
1. Resize the training images to 128 x 128.
2. Compute HoG features of size (32, 32, 8) for all training images.
3. Apply blocknorm in 4x4 cell neighborhoods.
4.
Fit a nearest neighbor classifier with three neighbors. Use
KNeighborsClassifier
from
sklearn library.
1.2
Classify test images
You are given a set of test images: two ‘Kraft’ logos and two ‘McDonald’s’ logos. These can be
found here: Q1/test’ folder. Examples can be seen in Figures 3 and 4. You are now asked:
1. Compute HoG features for all test images.
2. Display the HoG features for all test images.
3. Classify the test images using the classifier you built above.
4.
Does the classifier work on all test images? Discuss which properties of HoG features help
to classify the images. Which computer vision methods will you use for classifying logos
under rotation.
Figure 3: Example of Kraft test logo
Figure 4: Example of McDonald’s test logo
2
Image stitching (10 points)
You are given three different views of the same scene (check ‘Q2’ folder): image1.jpeg, image2.jpeg
and image3.jpeg (see Figure 5).
2
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After you submit, go back to check EAS to confirm that you just uploaded your assignment!
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