![]() ![]() The exam will focus on topics covered in the lectures. The presented version of the slides may include small changes and some slides may be skipped when presented. Recorded lecture videos will be uploaded after each lecture. ![]() Lecture slides and links to other materials can be found below. Programming skills and basic knowledge of data structures and mathematics (linear algebra, probability) will be necessary. There will be additional exams in the spring term. ![]() The main exam for the autumn edition of the course is in December. The dates of the exams are given in Sisu. Get more than 0 points from at least 8 weekly exercise rounds (see "Assignments"-page).The requirements for passing the course are as follows: Participation in the teaching sessions is not obligatory and not rewarded, but returning the homeworks is necessary and rewarded with bonus points. Thursday's sessions are usually located in Maari B classroom at Maarintalo.Īll the sessions and their locations can be found from the course calendar in M圜ourses. 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. The first exercise and deadline of weekly homework is on Friday September 9. The first lecture is on Monday September 5.Įxercises are on Fridays from 12:15 to 14:00 in room TU1 (TUAS building). Video recordings of the lectures will become available after the lecture on this M圜ourses page under "Lectures and materials" section. Lectures are given on Mondays from 8:15 to 10:00 in room T1 (CS building). The course will be lectured by Assistant Professor Juho Kannala ( ).Ĭourse personnel has emails of the form firstname.lastname aalto.fi (i.e. The course gives an overview of algorithms, models and methods, which are used in automatic analysis of visual data. Lecture 14: visual-inertial odometry / overviewThis 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. ![]() Lecture 13: object pose and shape estimation with shape priors Lecture 12: object motion segmentation and estimation Weekly team project meetings with tutors via Zoom/BBB, time slots by appointment. Live session for Q&A via Zoom during course slot. Lecture recording will be provided through Ilias. Lecture 10: object detection and pose estimation Live session for team project assignment and Q&A via Zoom during course slot.Įxercise 03: camera motion estimation, probabilistic state estimation, SLAM Exercise sheet 3 will be provided in Ilias. Lecture 07: probabilistic state estimation cont. Lecture 06: probabilistic state estimationĮxercise 02: dense motion estimation, two-view geometry Exercise sheet 2 will be provided in Ilias. Exercise sheet 1 will be provided through Ilias Lecture 01: introduction, course organization, image formation Please direct your questions about the course via email to Dr. Course capacity is limited and places will be assigned on a first come first serve basis. Please register to participate in the course through ILIAS. In the second half, lectures will be accompanied by team projects.įurther course information and materials can be found at the course entry in ILIAS. The first half of the course will contain lectures with exercises. This course will cover computer vision methods for 3D localization and scene reconstruction for intelligent systems. The course starts on April 20th, 2020 with a lecture.Ī detailed course schedule will be provided on this website. This lecture is part of the Intelligent Systems course series offered at the University of Tübingen by the MPI for Intelligent Systems.ĭue to the Covid-19 situation, until further notice the lectures will be provided online as recordings through ILIAS (lectures will not be held in the lecture halls) and the exercises will be conducted through video conferencing (details tba). ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |