CSCM77 Computer Vision and Deep Learning
Coursework
Released: 9 a.m., 13th March, 2020.
Due: 11 a.m., 27th April, 2020.
This coursework specification changed on 17th March, 2020. Please
ensure you have fully read the updated sections and understand the
new requirements.
1 Coursework Assignment: Pac-Man
1. To be completed by students working individually.
2. Feedback: You will receive feedback after your four-page report has been
marked.
3. Learning outcome: The tasks in this assignment are based on your
practical work in the lab sessions and understanding of the theories and
methods. Thus, through this coursework, you are expected to demonstrate
both practical skills and theoretical knowledge of several computer vision
techniques.
4. Unfair practice: This work is to be attempted individually. You may
request help from your lecturer, academic tutor and lab tutor, but you
may not collaborate with your peers.
5. Submission deadline: The code and your four-page report must be
submitted on Blackboard by 11 a.m. on Friday, 27th April, 2020.
You must submit your work to Blackboard before the deadline. Late
submissions without extenuating circumstances will receive zero marks.
6. You will be accessed on your four-page report. Your code will stand as
supporting material to your report.
7. This coursework constitutes 20% of your final mark for this module.
2 Task Description
In this coursework, you are given a set of 3D point-clouds with appearance
features (i.e. RGB values). These point-clouds were collected using a Kinect
system in our old PhD lab. Several virtual objects are also positioned among
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those point clouds. Your task is to write a Python programme that can automat-
ically detect those objects from an image and use them as anchors to navigate
through the 3D scene. A set of example images that contain those virtual ob-
jects are provided. These example images are used to train a random-forest
classifier (or a classifier of another kind explored in the labs) in order detect the
objects. Some Python code is provided to help you complete the task. Code
demonstrating how to obtain a 2D image by projecting 3D point-clouds onto the
camera image-plane, and how to re-position and rotate the camera, is provided
as well.
You will also write a four-page report on your work, which you will submit to
Blackboard alongside your code. As of 17th March, 2020, you will be accessed
on this report with your code as supporting material. The report must be
structured as an academic paper, with the following structure:
Introduction (10%). Contextualise the machine-learning problem and intro-
duce the task and the hypothesis. Make sure to include a few references to
previous work. You should demonstrate an awareness of the research-area.
Methodology (50%). The model(s) you trained to undertake the task. Any
decisions on hyperparameters must be stated here, including motivation
for your choices where applicable. If the basis of your decision is experi-
mentation with a number of parameters, then state this.
Results (30%). Describe, compare and contrast the results you obtained on
your model(s). Any relationships in the data should be outlined and
pointed out here. Only the most important conclusions should be men-
tioned in the text. By using tables and confusion-matrices to support the
section, you can avoid describing the results fully.
Discussion and Conclusion (10%). Restate the task and hypothesis/-ses con-
cisely. Reiterate the methods used. Describe the outcome of the experi-
ment and the conclusion that you can draw from these results in respect
of the hypothesis/-ses.
The following materials from lectures are relevant to this task:
1. Camera translation and orientation.
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2. Feature descriptors, e.g. histograms of pixel intensity, histograms of ori-
ented gradients, etc.
3. Supervised learning using random forests, convolutional neural networks,
region-proposal networks.
All the software and data are available on Blackboard.
2.1 Demo Code
Demo code is provided for orienting the camera view and obtaining an image
from the current camera view. The software to generate and visualise the point
clouds are also provided. The Python functions that are required to train and
test random forests are explained below.
Python provides a suite of methods for random-forest classification, namely
the sklearn.ensemble.RandomForestClassifier class, which is able to train
an ensemble for classification given some training observations and their labels.
Key uses of sklearn.ensemble.RandomForestClassifier include:
• To instantiate a random-forest object:
classifier = sklearn.ensemble.RandomForestClassifier(
n_estimators=n_trees)
• Fit the Random Forest to the data and labels:
classifier.fit(data, labels)
• Get predicted class probabilities given data:
prediction_probability = classifier.predict_proba(data)
The use of these functions will be further explained in the lab sessions.
Random-forest classification is required to detect those artificial objects
among the point clouds. Training should be carried out on the provided ex-
ample images. The detection requires a sliding-window-based evaluation that
is same as in human detection (HoG, covered in lectures). The sliding window
should be the same size as the training images.
You can use any features to train the classifier, for example, a histogram of
pixel values or histogram of oriented gradients. Once a virtual object is detected,
you need to move your camera to where the virtual object is located in space
and start your search for the next one until all virtual objects are found. In
the event that multiple virtual objects are detected in a single view, the nearest
virtual object should be selected.
2.2 Viva Voce
Following the termination of all face-to-face lectures at Swansea University on
Tuesday, 17th March, 2020, there will be no viva voces. You will be accessed
remotely based on your four-page report. You must however submit your code
alongside your report.
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2.3 Marking Criteria
Your report work will be assessed on its structure, content and presentation. We
expect it to be read as an academic paper, with the explanation appropriately
divided as per the structure described in the Task Description above. You
should demonstrate your knowledge of the field, along with any conclusions you
can draw from your results. We expect at least the same standard of work as
in CSC345, the prerequiste module for CSCM77.
2.4 Assessment
You will be accessed on the four-page report that you submit to Blackboard.
You must submit your code alongside your report, as this will act as supporting
material. This assignment is worth 20% of the total credit.
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