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.

    Teachers

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

    The course assistant is Xiaotian Li.

    Course personnel has emails of the form firstname.lastname @ aalto.fi (i.e. juho.kannala, xiaotian.li).

    Schedule

    Lectures are given on Mondays from 8:15 to 10:00 in room T1 (CS building). Video recordings of the lectures will become available after the lecture on this MyCourses page under "Lectures and materials" section. The first lecture is on Monday September 5. 

    Exercises are on Fridays from 12:15 to 14:00 in room TU1 (TUAS building). The first exercise and deadline of weekly homework is on Friday September 9.

    The deadline of weekly homework exercises is at noon on Fridays and the solutions are presented in Friday's exercise sessions. In addition, there is a guidance session every Thursday from 14:15 to 16:00 where teachers are available to give instructions for solving the homework. Thursday's sessions are usually located in Maari B classroom at Maarintalo. 

    All the sessions and their locations can be found from the course calendar in MyCourses. Participation in the teaching sessions is not obligatory and not rewarded, but returning the homeworks is necessary and rewarded with bonus points.

    Registration and requirements

    Aalto students should register via Sisu. 

    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 Sisu. The main exam for the autumn edition of the course is in December. There will be additional exams in the spring term. 


    Pre-requisites

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