Topic outline

  • General

    Course description

    This course provides an introduction to computer vision including fundamentals of image formation & filtering, feature detection & matching, structure-from-motion & image-based 3D modelling, motion estimation & tracking, and object detection & recognition. The course gives an overview of algorithms, models and methods, which are used in automatic analysis of visual data.

    The course is organized remotely so that lectures and exercises are online video meetings. 

    Teachers

    The course will be lectured by Assistant Professor Juho Kannala (https://users.aalto.fi/~kannalj1/).

    The course assistants are Antti Parviainen and Pauliina Paavilainen.

    Course personnel has emails of the form firstname.lastname @ aalto.fi (i.e. juho.kannala, antti.parviainen, pauliina.paavilainen).

     

    Schedule

    Lectures are given on Mondays from 10:15 to 12:00 via Zoom. The first lecture is on Monday September 7.

    Exercises are presented on Fridays from 12:15 to 14:00 via Zoom. The first exercise session and deadline of weekly homework is on Friday September 11.

    The deadline of weekly homework exercises is at noon on Fridays and the solutions are presented in Friday's exercise sessions. 

    All the sessions are given online via Zoom and recordings are made available afterwards. Participation in the teaching sessions is not obligatory and not rewarded, but returning the homeworks is required and rewarded with bonus points.


    Registration and requirements

    Aalto students should register via Oodi. 

    The requirements for passing the course are as follows:

    • Get more than 0 points from at least 8 weekly exercise rounds (see "Assignments"-page)
    • Pass the exam


    Exams

    The dates of the exams are given in Oodi. The main exam for the autumn edition of the course is in December. There will be additional exams in the spring term. Details of the format of the exam will be given later.


    Pre-requisites

    Programming skills and basic knowledge of data structures and mathematics (linear algebra, probability) will be necessary.