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# Table of Contents

1. [A Gentle Introduction](#orgf9d4f1c)
1. [Lecture Information](#org33b5f32)
1. [Assignments](#orgf7e9b9e)
2. [The Lecture Structure](#orgb5cae0e)
3. [Code Supplement](#org94a0f06)
4. [Reading List](#org159a80f)
1. [A Gentle Introduction](#org6bdbe1a)
1. [Lecture Information](#orga66fad8)
1. [Assignments](#org8ab78d2)
2. [The Lecture Structure](#org6649915)
3. [Code Supplement](#org1715c47)
4. [Reading List](#orgddf167e)


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# A Gentle Introduction

Expand All @@ -22,7 +22,7 @@ focus will be on the topics of:
4. An example of using ML in image recognition techniques.


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## Lecture Information

Expand Down Expand Up @@ -118,7 +118,7 @@ The details of the lecture are given below.
</table>


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### Assignments

Expand Down Expand Up @@ -161,135 +161,135 @@ The grade breakdown is as follows:
</tbody>
</table>

1. Individual Assignment

An individual assignment will be given to you to work on. This assignment will consist of
questions pertaining to concepts and image processing techniques.
The grade breakdown is as follows:
<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
<colgroup>
<col class="org-left" />
<col class="org-right" />
</colgroup>
<thead>
<tr>
<th scope="col" class="org-left">DEFINITION</th>
<th scope="col" class="org-right">GRADE (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td class="org-left">Report Style</td>
<td class="org-right">15</td>
</tr>
<tr>
<td class="org-left">Q1 - Blurring Filters</td>
<td class="org-right">15</td>
</tr>
<tr>
<td class="org-left">Q2 - Image Channel Analysis</td>
<td class="org-right">15</td>
</tr>
<tr>
<td class="org-left">Q3 - RNG Map Generation</td>
<td class="org-right">10</td>
</tr>
<tr>
<td class="org-left">Q4 - Image Cleaning</td>
<td class="org-right">10</td>
</tr>
<tr>
<td class="org-left">Q5 - Shape Recognition</td>
<td class="org-right">30</td>
</tr>
<tr>
<td class="org-left">Q6 - Image Quality Comparison</td>
<td class="org-right">10</td>
</tr>
<tr>
<td class="org-left">Sum</td>
<td class="org-right">100</td>
</tr>
</tbody>
</table>
**NOTE:** The assignment is individual and is not meant to be worked as a group. Once the
code and the work is submitted it will be vetted against a software to determine
if any collusion has occured.

2. Group Assignment

The group assignment focuses on a student defined project which its presentation will be
done in the last 3 sessions of the course. You are to come up with a group and a project
within the first 3 weeks of the lecture otherwise one will be given to you.
The grade breakdown is as follows:
<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
<colgroup>
<col class="org-left" />
<col class="org-right" />
</colgroup>
<thead>
<tr>
<th scope="col" class="org-left">DEFINITION</th>
<th scope="col" class="org-right">GRADE (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td class="org-left">Report Style</td>
<td class="org-right">15</td>
</tr>
<tr>
<td class="org-left">Content</td>
<td class="org-right">55</td>
</tr>
<tr>
<td class="org-left">Q &amp; A</td>
<td class="org-right">30</td>
</tr>
</tbody>
</table>
In report writing students must declare their contribution to the work and they will be
asked regarding their field of work during the Q&A (i.e., if Student A has worked with
blurring filter he may be asked on why a specific one is chosen and/or the concepts and
maths behind the said filter).
**NOTE:** Students will be graded based on their contribution to the project and answers
during the Q&A, therefore will be graded individually.


<a id="orgb5cae0e"></a>
**Individual Assignment**

An individual assignment will be given to you to work on. This assignment will consist of
questions pertaining to concepts and image processing techniques.

The grade breakdown is as follows:

<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">


<colgroup>
<col class="org-left" />

<col class="org-right" />
</colgroup>
<thead>
<tr>
<th scope="col" class="org-left">DEFINITION</th>
<th scope="col" class="org-right">GRADE (%)</th>
</tr>
</thead>

<tbody>
<tr>
<td class="org-left">Report Style</td>
<td class="org-right">15</td>
</tr>


<tr>
<td class="org-left">Q1 - Blurring Filters</td>
<td class="org-right">15</td>
</tr>


<tr>
<td class="org-left">Q2 - Image Channel Analysis</td>
<td class="org-right">10</td>
</tr>


<tr>
<td class="org-left">Q3 - RNG Map Generation</td>
<td class="org-right">10</td>
</tr>


<tr>
<td class="org-left">Q4 - Image Cleaning</td>
<td class="org-right">10</td>
</tr>


<tr>
<td class="org-left">Q5 - Shape Recognition</td>
<td class="org-right">30</td>
</tr>


<tr>
<td class="org-left">Q6 - Image Quality Comparison</td>
<td class="org-right">10</td>
</tr>


<tr>
<td class="org-left">Sum</td>
<td class="org-right">100</td>
</tr>
</tbody>
</table>

**NOTE:** The assignment is individual and is not meant to be worked as a group. Once the
code and the work is submitted it will be vetted against a software to determine
if any collusion has occured.

**Group Assignment**

The group assignment focuses on a student defined project which its presentation will be
done in the last 3 sessions of the course. You are to come up with a group and a project
within the first 3 weeks of the lecture otherwise one will be given to you.

The grade breakdown is as follows:

<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">


<colgroup>
<col class="org-left" />

<col class="org-right" />
</colgroup>
<thead>
<tr>
<th scope="col" class="org-left">DEFINITION</th>
<th scope="col" class="org-right">GRADE (%)</th>
</tr>
</thead>

<tbody>
<tr>
<td class="org-left">Report Style</td>
<td class="org-right">15</td>
</tr>


<tr>
<td class="org-left">Content</td>
<td class="org-right">55</td>
</tr>


<tr>
<td class="org-left">Q &amp; A</td>
<td class="org-right">30</td>
</tr>
</tbody>
</table>

In report writing students must declare their contribution to the work and they will be
asked regarding their field of work during the Q&A (i.e., if Student A has worked with
blurring filter he may be asked on why a specific one is chosen and/or the concepts and
maths behind the said filter).

**NOTE:** Students will be graded based on their contribution to the project and answers
during the Q&A, therefore will be graded individually.


<a id="org6649915"></a>

## The Lecture Structure

Expand Down Expand Up @@ -432,7 +432,7 @@ methods in improving/analysing gathered images. The structure of the lecture is
</table>


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## Code Supplement

Expand All @@ -442,7 +442,7 @@ it is not feasible to fit all the content of the code to the slides and it is ea
[Visit the Code Supplement Website](https://dtmc0945.github.io/L-MCI-BSc-Digital-Image-Processing/)


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## Reading List

Expand All @@ -456,5 +456,3 @@ mandatory.
3. Nixon M. et. al "Feature Extraction and Image Processing for Computer Vision" Academic press 2019
4. Gonzalez R. "Digital Image Processing" Pearson 2009.

**White Papers**

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