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title: AIS2204 Maskinsyn
categories: Module

+ See [Lecture Notes](Overview) for learning materials
+ See [Blackboard]( for announcements and 
  discussion boards.

# Practical Information

1.  There are no compulsory exercises.  It is your responsibility to
    the exercises you need to do to understand the subject and gain
2.  Feedback is provided *in class* upon demonstration of own work
    and solutions.
3.  The module emphasises the relation between theoretical and practical

## How to work with the module

1.  Read the theory.
2.  Do practical exercises to *test your theoretical understanding*
3.  Evaluate your own solutions and reflect upon
    - what have you learnt from the exercise?
    - what do you yet not know?
4.  Don't do a lot of exercises quickly.
    It is better to make sure that you comprehend a few exercises fully,
    and can justify and validate your own reasoning.
5.  Ask Questions.  
    I will generally not repeat material unsolicited, 
    but I am very happy to discuss any question you may have.
4.  Keep a diary.
    Make sure you can refer back to previous ideas and reuse previous solutions.

## The practical exercises

The practical exercises are designed to give both

1. standalone prototypes 3-5 times during the semester,
   each demonstrating key aspects of the theory.
2. combine together into a final machine vision system,
   rudimentary but complete.

## How does the exam work

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

+ Capacity: 30 candidates
+ [Assessment Guide]()

# Syllabus

+ From 
  [Ma (2005): An Invitation to 3-D Vision: From Images to Geometric Models]
    + Chapters 1-6 and 11
    + Parts of Chapters 7-10 and 12 to be announced later.
+ All lectures and exercises
+ Additional reading:
    + *OpenCV 3 Computer Vision with Python Cookbook* by
      Alexey Spizhevoy (author) from O'Reilly can be a useful supplement.
      Search for it in [Oria](
      There is an e-book available.