# AIS2204 Maskinsyn

- See Lecture Notes for learning materials
- See Blackboard for announcements and discussion boards.

# Practical Information

- There are no compulsory exercises. It is your responsibility to do the exercises you need to do to understand the subject and gain experience.
- Feedback is provided
*in class*upon demonstration of own work and solutions. - The module emphasises the relation between theoretical and practical understanding.
- I try to give you
*freedom to learn*. Please use that freedom to learn what you need to know.

## How to work with the module

- Read the theory.
- Do practical exercises to
*test your theoretical understanding*.- Do the exercises that you need to do to make sense of the theory.

- Evaluate your own solutions and reflect upon
- what works and what doesn’t?
- what have you learnt from the exercise?
- what do you yet not know?

- Don’t do a lot of exercises quickly. It is better to reflect on a few exercises fully done, than to do a lot without thinking.
- Ask Questions.

I will generally not repeat material unsolicited, but I am very happy to discuss any question you may have. - Keep a diary. Make sure you can refer back to previous ideas and reuse previous solutions.

## The practical exercises

Both practical and theoretical exercises are given. Most of the exercises are designed in the hope that you can complete them in a session or two, but also move on even if you do not complete them.

Observe that the exam gives you a lot of freedom to emphasise what you find interesting and useful. Thus you will be rewarded for solving related variants and for tying different exercises together in more complete systems.

## How does the exam work

- Oral Exam.
- You get seven minutes to demonstrate the highlights of your understanding of the subject.
*Make a case for the grade you think you deserve.* - The examiner will use the rest of the time for questions to clarify and to demonstrate expected breadth and depth.
- Note that there are both theoretical and practical learning outcomes, and the module emphasises the relation between these two.

- Capacity: 30 candidates
- Assessment Guide. This will be reviewed in the reference group and only made final at the end of the teaching term.

# Syllabus

- The syllabus is defined by the lectures and exercises.
- Two textbooks will be used, both available electronically for free:
- Ma (2005): An Invitation to 3-D Vision: From Images to Geometric Models
- Chapters 1-5 and 11
- This book gives a very good presentation of the fundamental theory, but it is unfortunately outdated when it comes to current applications.

- Szeliski (2022): Computer Vision: Algorithms and Applications, 2nd ed.
- Chapter 5.1-5.4.
- Cursory Chapters 5-6.
- Chapter 7.1-7.4. (This partly overlaps with chapters from Ma (2005), but the coverage of feature descriptors is particularly important.)
- This book is up to date and covers feature descriptors and machine learning.

Unfortunately it does not always describe the algorithms in detail.

- Ma (2005): An Invitation to 3-D Vision: From Images to Geometric Models
- Additional reading:
- Alexey Spizhevoy (2018)
*OpenCV 3 Computer Vision with Python Cookbook*Available electronically from the university library; see Oria.

- Alexey Spizhevoy (2018)