Computer Vision and Imaging
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1. Instructions
You are to work individually for this assessment, and produce one report. The report must be no more than THREE
pages long including all graphs and tables. The report should be submitted via CANVAS (See Canvas for deadline).
Your assignment involves some implementation and some experimentation, plus the write up. All images to be
processed are provided.
Your code/script should also be submitted as supplementary data (5 extra Pages max): Remember to follow good
practice with structure, use of functions and adding of comments to your code.
Any text beyond the stated 3 pages will not be marked. There will be 5% penalty per day (or part of), for late
submissions.
Your submitted assignment should:
• Be a maximum of 3 pages (A4)
• Have minimum margins (top/bottom/sides) of 2 cm
• Use Arial (narrow) font of 11
• Contain your student ID, and module code in the header
2. The assignment:
Task 1. Laplacian of Gaussian [10 Marks]
• Write a function to create a mask that implements the Laplacian of Gaussian filter. This is the second
derivative of the Gaussian noise function. We covered this operator in detail in the lectures and you can
read more about it in the Chapter on Edge Detection in textbook by Jain or other related text within the
resources section of canvas: https://canvas.bham.ac.uk/courses/72993/modules/items/3498608
• Show the results of applying your filter to the ‘Shakey’ image, outlining the steps taken, and discussing
your findings. Discuss the use and method of any noise removal technique used.Task 2: Cell Detection: Background
• Serological testing for anti-nuclear antibodies (ANAs) plays an integral role in diagnosing a plethora of
autoimmune diseases. ANAs are detected by indirect immunofluoresence (IIF) on HEp-2 cells, a human
epithelial cell line originally derived from a larynx carcinoma. In brief, patient serum (containing ANAs) is
incubated on HEp-2 cells and detected with a commercially produced fluorescein conjugated anti-human
secondary antibody. When viewed under a fluorescent microscope, the distinct patterns - a result of the
ANAs targeting the nuclear components of HEp-2 cells - can then be associated with a specific disease.
• A semi-quantitative evaluation of fluorescent intensity can be obtained by conducting serial dilutions of
the serum sample to endpoint (the final antibody titre where positive staining is still visible). However, this
methodology is still reliant on subjective interpretation and therefore prone to both high inter- and intra
laboratory variance. As such, standardization of ANA testing by IIF remains a significant issue.
• You will find THREE images of fluorescing cells provided, together with manually detected edges (‘Ground
Truth’). You need to devise, carry out and test the efficacy of the various edge detectors we have seen.
These are:
1. Roberts,
2. Sobel,
3. first order Gaussian,
4. Laplacian,
5. Laplacian of Gaussian.
Task 2: Edge Detection [15 Marks]
• Apply the above edge detectors to the THREE sets of images provided. Show the results, outlining the steps
taken and discuss your findings. Discuss the use and method of any noise removal technique used.
Task 3: Advanced Edge Detection [15 Marks]
• Implement any other edge detector algorithm to the THREE sets of images provided. Show the results,
outlining the steps taken and discuss your findings. Discuss why the use of the proposed algorithm is
better/worse than those in Task 2.