School of Engineering

UCLan Coursework Assessment Brief

Module Title: Computer Vision Module Code: EL3105

Flip-book Animations

Academic Year: 2022-23 Level: 6

   This assessment is worth 50% of the overall module mark

    THE BRIEF/INSTRUCTIONS

      First three images from the “CVML Clock” dataset

  This assignment is designed to give you an insight into selected aspects of computer vision applied to image feature extraction, feature matching, aligning multiple images, image warping, and construction of an animated image objects. You are asked to solve various tasks including detection of image features and their robust matching, write computer vision software as well as test your solution and interpret the results.

This assignment will enable you to:

• Deepen your understanding of the features/keypoints detection and robust matching between

features, image registration, and creation of animated objects.

• Recognize software design challenges behind implementations of computer vision algorithms.

• Design and optimise software to meet specified requirements.

• Acquire a hands-on understanding of image registration and warping.

(These correspond to point 1, 2, 4 and 5 of the module’s learning outcomes)

Assignment Description and Objectives

You are asked to write software (in matlab or/and python) to implement feature-based image alignment for construction of a digital flip-book animation for a set of fourteen images available in the “CVML Clock” dataset. You should consider implementing following processing steps:

• Extraction of image features.

• Computation of feature descriptors.

• Matching extracted features using computed image descriptors.

• Computation of an optimal transformations between images based on the matched features.

• Resample all the images onto a common coordinate frame.

• Convert the resulting image sequence into an animated GIF.

You should explore different design options, including selection of features, descriptors, and transformation models. You should write the software using matlab or python. The CVML_Clock.zip file containing the “CVML Clock” dataset is available from the module materials tab in the EL3105 Blackboard area. The first three images, from the “CVML Clock” dataset, are shown in the figure below.

 Marking scheme

Your report should contain the following elements; it will be marked in accordance with the following marking scheme:

  Item

1. Features (keypoints) detection

2. Computation of the detected features descriptors

3. Selection of the image transformation model

4. Matching between image

5. Construction of flip-book animation

6. Evaluation and critical analysis of the results

7. Presentation of the report

Total

Weight (%)

    15

    15

    15

    15

    10

    20

    10

100

                   References:

This assignment is based on the exercise 8.1 (page 549) from the second edition of Richard Szeliski’s “Computer Vision Algorithms and Applications”.

R. Hartley, A. Zisserman, "Multiple View Geometry in Computer Vision," Cambridge University Press, 2003.

H. Bay, T. Tuytelaars, L.V. Gool, “SURF: Speed Up Robust Features”, European Conference on Computer Vision , ECCV’2006, pp. 404-417. 2006.

H. Al-Sahaf, et al., “Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-Invariant Texture Image Descriptors”, IEEE Transactions on Evolutionary Computations, Vol. 21, No. 6, pp. 825-844, 2017.

Matlab help on: “Object Detection in Cluttered Scene Using Point Feature Matching”.

      PREPARATION FOR THE ASSESSMENT

The assignment is to be introduced and discussed during the class on Tuesday 24th of Januray as well as 24th of February and 3rd of March labs. During those sessions the background of this assignment will be introduced; the data structure will be explained, and the expected results will be elucidated with examples. The set of software tools available for the assignment will be also described.

All the algorithmic aspects necessary for the successful completion of the assignment were or will be covered during the lectures, tutorial, and laboratory sessions, these include feature/keypoint detection, descriptor calculation, robust matching, and estimation of a transformation aligning matched features.

    RELEASE DATES AND HAND IN DEADLINE

Assessment Release date: 24/01/2023 Assessment Deadline Date and time: 28/04/2023 - 23:59

Please note that this is the final time you can submit – not the time to submit! Feedback for this assessment will be provided by 19/05/2023.

   SUBMISSION DETAILS

Submission of assignment work

This assignment constitutes 50% of the total module assessment mark. You should write a report for this assignment documenting your solutions for the tasks defined above. The report should include a very brief introduction describing the problem, description of your adopted solutions, a more extensive description of the results and conclusions section summarising the results. The report should be approximately 1500 words long plus relevant materials (References and Appendices). You should use Harvard referencing system for this report.

   The report should be submitted electronically to “Flip-book Animations Assignment” Turnitin through Blackboard.

You should submit a documented matlab/python code solving the defined above tasks. The code should be self- contained, i.e., it should be able to run as it is, without a need for any addition tools/libraries. You might be asked to explain operation of your software. The code should be submitted separately from the report into Blackboard EL3105 assignment area denoted as “Flip-book Animations Assignment Code and Animations”.

You are also expected to submit the resulting animation gif file, demonstrating performance of your solution on the “CVML Clock” data

The gif file should be submitted together with the code (in a single zip file) into Blackboard EL3105 assignment area denoted

as “ Flip-book Animations Assignment Code and Animations”.

Late work

Work submitted electronically may be submitted after the deadline to the same Turnitin assignment slot and will be automatically flagged as late.

Penalties for late submission

Except where an extension of the hand-in deadline date has been approved lateness penalties will be applied in accordance with University policy as follows:

 (Working) Days

1 - 5

more than 5

Plagiarism

Late Penalty

maximum mark that can be achieved: 40% 0% given

provided as part of this assignment. This gif animation is an integral part of the

 assignment, up to 50 marks could be deduced in case this gif file is missing from your submission.

During the induction and via your student handbook, you were informed of the serious consequences of using or attempting to use unfair means to enhance performance. This includes plagiarism. The work submitted must be your own and any information and material used properly identified and acknowledged.

The University operates an electronic plagiarism detection service where your work may be uploaded, stored and cross-referenced against other material. The software searches the World Wide Web and extensive databases of reference material to identify duplication.

For detailed information on the procedures relating to plagiarism, please see the current version of the University Academic Regulations.

    HELP AND SUPPORT

• The support for this assignment will be provided during scheduled lab sessions.

• For support with using library resources, please contact Science & Technology subject senior librarian Mr. Neil Marshall

<[email protected]> or <[email protected]>. You will find links to lots of useful resources in the My

Library tab on Blackboard.

• If you have not yet made the university aware of any disability, specific learning difficulty, long-term health or mental

health condition, please complete a Disclosure Form. The Inclusive Support team will then contact to discuss

reasonable adjustments and support relating to any disability. For more information, visit the Inclusive Support site.

• To access mental health and wellbeing support, please complete our online referral form. Alternatively, you can email

[email protected], call 01772 893020 or visit our UCLan Wellbeing Service pages for more information.

• If you have any other query or require further support you can contact The <i>, The Student Information and Support Centre. Speak with us for advice on accessing all the University services as well as the Library services. Whatever your query, our expert staff will be able to help and support you. For more information, how to contact us and our opening

hours visit Student Information and Support Centre.

If you have any valid mitigating circumstances that mean you cannot meet an assessment submission deadline and you wish to request an extension, you will need to apply online prior to the deadline.

Disclaimer: The information provided in this assessment brief is correct at time of publication. In the Version: 1 unlikely event that any changes are deemed necessary, they will be communicated clearly via e-mail and a

new version of this assessment brief will be circulated.