https://github.com/mantasu/uob-cv

My notes and exercises for UoB CV module
https://github.com/mantasu/uob-cv

computer-vision imaging latex matlab uob

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My notes and exercises for UoB CV module

• Host: GitHub
• URL: https://github.com/mantasu/uob-cv
• Owner: mantasu
• Created: 2022-02-23T18:04:57.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2022-05-25T04:09:06.000Z (over 2 years ago)
• Last Synced: 2024-11-09T08:12:58.356Z (about 2 months ago)
• Topics: computer-vision, imaging, latex, matlab, uob
• Language: MATLAB
• Homepage:
• Size: 24.3 MB
• Stars: 6
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Computer Vision Notes and Exercises

Computational Vision & Imaging

University of Birmingham - Spring 2022


## Overview

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.md` file should be checked with an enhanced markdown previewer, such as VS code (using extension [Markdown All in One](https://marketplace.visualstudio.com/items?itemName=yzhang.markdown-all-in-one)) for colors and latex equations.

## Solutions

### Labs

* [Lab 1](lab1/report.pdf): Edge Detectors. (_Done_)

* [Lab 2](lab2/report.pdf): Noise Removal. (_Done_)

* [Lab 3](lab3/report.pdf): Hough Transform. (_Done_)

## Feedback

### Lab 1

| 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 |

## Examples

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.




```matlab

% 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")

```