Computer Vision

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Unit Title: Computer Vision
Resubmission Assessment Title: Definition and solution of a real-world Computer Vision
Unit Level: 7
Assessment Number: 1 of 1
Credit Value of Unit: 20
Date Issued: 09/07/2021
Marker(s): Prof. Marcin Budka
Submission Due Date: 13/08/2021 Time: 12.30pm
Quality Assessor:
Dr Rashid Bakirov
Submission Location: Jupyter notebook (Brightspace)
Video Presentation (Brightspace)
Peer Assessment form (Brightspace)
Feedback method: Brightspace
This can be an individual or group assignment which carries 100% of the final unit mark
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including screen capture rather than just a “talking head”. This element is mandatory, and marks will
only be awarded to those who submit the video.
Important: if your video is longer than 10 minutes, your mark will be based on the first 10 minutes of
the video only. You should hence carefully plan the content of your video and rehearse and time your
presentation. You can also use video editing software when putting it together.
3. Peer-assessment of the group members (Brightspace, only if you were part of a group)
Each member of each group will be asked to anonymously assess the contributions to the task of all
the other members in their respective group via Brightspace. It is very important that you’re honest in
your assessment. The final mark will be weighted by your contribution. For example, if in a group of 2
you all contributed equally, your final mark will be the same. This element is mandatory (unless you
approached the assignment individually), and marks will only be awarded to those who submit the
peer-assessment form.
Your resubmission should be equivalent to 3,000 words per person, hence we expect group submissions
to address more complex and challenging problems than individual submissions.
The following criteria will be used to assess the assignment. Each criterion will be considered according
to the Level 7 Grade Range (Masters Level), found in the 6F – Generic Assessment Criteria: Procedure.
Mark %
Definition of the problem:
– Context
– Rationale
– Aims and objectives
Data acquisition and preparation:
– Data characteristics and statistics
– Data visualisation
– Data processing
– Choosing appropriate techniques
– Well-organised approach
– Subsequent steps informed by earlier findings
– Automation (i.e. avoiding manual steps)
– Comparison against baseline(s)
– Visualisations
– Error analysis
– Discussion and achievement of objectives
– Reproducibility
– Structure and organisation
– Coding conventions and principles
– Scholarship and clarity
– Referencing
Video presentation:
– Quality
– Delivery
1, 2, 3, 4
The following sections describe what are the expectations for each level of achievement:
To achieve a Pass:
Define a moderately challenging computer vision problem. Apply and evaluate at least two different
computer vision approaches (e.g. two different neural network architectures), compare and contrast their
performance/applicability for the problem at hand with the help of basic data visualisation techniques. The
notebook will facilitate some reproducibility, with reasonably clear structure and organisation, with some
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To achieve a higher mark:
Define a more challenging computer vision problem. Apply and evaluate a range of computer vision
approaches, including hybrids of traditional and neural networks. Robustly compare and contrast their
performance/applicability for the problem at hand with the help of more advanced (e.g. interactive) data
visualisation techniques. The notebook will facilitate full reproducibility, with clear structure and
organisation, and exhaustive referencing.
This assignment tests your ability to demonstrate:
1. knowledge and understanding of Computer Vision techniques and their applications,
2. critical awareness of the strengths and limitations of various techniques and the classes of problems
to which they may be effectively applied,
3. ability to provide expert advice by matching specific techniques to business objectives,
4. competence in use of the introduced techniques, approaches and tools, together with the ability to
correctly interpret and evaluate the results.
You are encouraged to ask questions about the brief as early as possible, giving you the opportunity to
achieve the best marks possible without any delay. You are invited to ask questions during timetabled
sessions and electronically.
Signature Marker: Marcin Budka
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• If a piece of resubmission coursework is not submitted by the required deadline, the following
regulation will apply:
‘Failure to submit/complete any other types of coursework (which includes resubmission coursework
without exceptional circumstances) by the required deadline will result in a mark of zero (0%) being
The Standard Assessment Regulations can be found on Brightspace.
• If you have any valid exceptional circumstances which mean that you cannot meet an assignment
submission deadline and you wish to request an extension, you will need to complete and submit the
Exceptional Circumstances Form for consideration to your Programme Support Officer (based in
C114) together with appropriate supporting evidence (e.g, GP note) normally before the
coursework deadline. Further details on the procedure and the exceptional circumstances form can
be found on Brightspace. Please make sure that you read these documents carefully before
submitting anything for consideration. For further guidance on exceptional circumstances please see
your Programme Leader.
• You must acknowledge your source every time you refer to others’ work, using the BU Harvard
Referencing system (Author Date Method). Failure to do so amounts to plagiarism which is against
University regulations. Please refer to for the University’s guide to citation in the Harvard style. Also be aware of Self-plagiarism, this
primarily occurs when a student submits a piece of work to fulfill the assessment requirement for a
particular unit and all or part of the content has been previously submitted by that student for formal
assessment on the same/a different unit. Further information on academic offences can be found on
Brightspace and from

Students with Additional Learning Needs may contact Learning Support on
Disclaimer: The information provided in this assignment brief is correct at time of publication. In the
unlikely event that any changes are deemed necessary, they will be communicated clearly via e-mail and
Brightspace and a new version of this assignment brief will be circulated.

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