COMP9517: Computer Vision
2023 T3 Lab 2 Specification
Maximum Marks Achievable: 2.5
This lab is worth 2.5% of the total course marks.
Objective: This lab revisits important concepts covered in the lectures of Week 3 and aims to
make you familiar with implementing specific algorithms.
Materials: The sample image to be used in the tasks of this lab is available in WebCMS3. You
are required to use OpenCV 3+ with Python 3+.
Submission: The tasks are assessable after the lab. Submit your source code as a Jupyter
notebook (.ipynb) which includes all output (see coding requirements below) by the above
deadline. The submission link will be announced in due time.
The sample image Hallstatt.png is to be used for all questions.
SIFT: Scale-Invariant Feature Transform
A well-known algorithm in computer vision to detect and describe local features in images is
the scale-invariant feature transform (SIFT). Its applications include object recognition,
mapping and navigation, image stitching, 3D modelling, object tracking, and others.
A SIFT feature of an image is a salient keypoint with an associated descriptor. SIFT computation
is commonly divided into two steps:
1) detection,
2) description.
At the end of the detection step, for each keypoint the SIFT algorithm computes the:
• keypoint spatial coordinates (x, y),
• keypoint scale (in the scale space),
• keypoint dominant orientation.
The lab files should be submitted online.
Instructions for submission will be posted closer to the deadline.
Deadline for submission is Week 4, Wednesday 4 October 2023, 18:00:00.
The subsequent description step computes a distinctive 128-dimensional feature vector for
each keypoint. SIFT is designed in such a way that this descriptive feature vector is invariant
to scaling and rotation. Moreover, the algorithm offers decent robustness to noise,
illumination gradients, and affine transformations.
Task 1 (0.5 mark): Compute the SIFT features of the given image.
a) Extract SIFT features with default parameters and show the keypoints on the image. Hint:
There are existing OpenCV functions you can use for this (see suggestions below).
b) To achieve better visualization of the keypoints, reduce the number of keypoints.
Hint: Vary the parameter contrastThreshold or nfeatures so that the number of keypoints
becomes about 10% of all default keypoints.
Show the result images obtained in a) and b) in your Jupyter notebook and include a brief
description of the approach you used for b).
Task 2 (0.8 mark): Distort the image and recompute the SIFT features.
a) Add pepper noise to the given image. Hint: The scikit-image library has a utility function to
add random noise of various types to images.
b) Extract the SIFT features from the noisy image and show the keypoints using the same
parameter setting as for Task 1 (for the reduced number of keypoints).
c) Inspect the keypoints visually: Are the keypoints of the noisy image roughly the same as
those of the original image? What does this imply?
Show the result images obtained in a) and b) in your Jupyter notebook and include your
answers to the questions in c).
Task 3 (1.2 mark): Transform the image and recompute the SIFT features.
a) Transform the original image by first enlarging it with a scaling factor of 120% and then
rotating the scaled image clockwise by 60 degrees.
b) Extract the SIFT features from the transformed image and show the keypoints using the
same parameter setting as for Task 1 (for the reduced number of keypoints).
c) Inspect the keypoints visually: Are the keypoints of the transformed image roughly the
same as those of the original image? What does this imply?
d) Match the SIFT descriptors of the keypoints of the transformed image with those of the
original image using the nearest-neighbour distance ratio method. Show the keypoints of
the 5 best-matching descriptors on both the original and the transformed image.
Hint: Brute-force matching is available in OpenCV for feature matching.
Show the result images obtained in a), b) and d) in your Jupyter notebook and include your
answers to the questions in c).
Coding Requirements and Suggestions
Check the OpenCV documentation for various built-in functions to find SIFT features, draw
keypoints, and match keypoints in images. You should understand how the algorithm works,
what parameters you can set in these built-in functions, and how these parameters affect the
output. For your reference, below are links to relevant OpenCV functions.
2D Features Framework
https://docs.opencv.org/4.6.0/da/d9b/group__features2d.html
Drawing Functions of Keypoints and Matches
https://docs.opencv.org/4.6.0/d4/d5d/group__features2d__draw.html
Descriptor Matchers
https://docs.opencv.org/4.6.0/d8/d9b/group__features2d__match.html
OpenCV SIFT Class Reference
https://docs.opencv.org/4.6.0/d7/d60/classcv_1_1SIFT.html
In your Jupyter notebook, the input image must be readable from the location specified as an
argument, and all output images and other requested results must be shown in the notebook
environment. All cells in your notebook must have been executed so that the tutor/marker
does not have to execute the notebook again to see the results.
Refer to the following page to understand image features and various feature detectors:
https://docs.opencv.org/4.6.0/db/d27/tutorial_py_table_of_contents_feature2d.html
Also, refer to the following example of computing SIFT features and showing the keypoints:
https://docs.opencv.org/4.6.0/da/df5/tutorial_py_sift_intro.html
And finally see this page for an example of feature matching:
https://docs.opencv.org/4.6.0/dc/dc3/tutorial_py_matcher.html
Reference: D. G. Lowe. Distinctive image features from scale-invariant keypoints.
International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, November 2004.
https://doi.org/10.1023/B:VISI.0000029664.99615.94
Copyright: UNSW CSE COMP9517 Team. Reproducing, publishing, posting, distributing, or
translating this lab assignment is an infringement of copyright and will be referred to UNSW
Student Conduct and Integrity for action.
Released: 27 September 2023