Computational Vision & Imaging
University of Birmingham - Spring 2022
This repository contains my notes and lab exercises for the Computational Vision & Imaging module provided by the University of Birmingham. Lab exercises contain MATLAB code which is also rendered as HTML document for each lab session for visual reasons. This is my personal practice of writing LaTeX documents and coding in MATLAB.
Note:
notes.mdfile should be checked with an enhanced markdown previewer, such as VS code (using extension Markdown All in One) for colors and latex equations.
| Criteria | Comments |
|---|---|
| QUESTION 1: What do you notice regarding the effect of changing the threshold? | Excellent answer and very well put together! |
| QUESTION 2: What do you notice regarding the difference between Sobel and Roberts? | Great observations and explanations. Additional points to consider are Roberts can better detect “\” and “/” edges due to the values within the filter. Computationally Roberts is faster to compute than Sobel because it is a 2x2 matrix while Sobel is a 3x3 matrix Finally, observe the values in the SobelX and SobelY are weighted closer to the centre pixel which reduces noise within the feature map. |
| QUESTION 3: What do you notice regarding the difference between magnitude and absolute when calculating the edge? | Excellent answer again and very well presented. Please submit snippets of your code in Appendix for us to give feedback |
Lab question example
Question: what happens to the edges in the heavily blurred case after applying gradient operators, such as Sobel operators?
Answer: thicker edges stand out more and thinner edges almost disappear. This is because thin edges may sometimes be confused with noise which gets averaged out after heavily blurying an image. Strong edges, on the other hand, remain standing out (i.e., they are surrounded by more values indicating an edge region) therefore the gradient operator captures the changes and emphasizes them.
Lab code example
Task: combine the idea of the Laplacian operator with the idea of Gaussian smoothing.
% Get LoG filter, apply to image and apply zerocross
log_filter = conv2(laplacian, gaussian_filter_5x5);
I_in = conv2(log_filter, shakey);
I_out = edge(I_in, 'zerocross');
% Show the result
show_image(I_out, "log_zerocross")