GIS-E3020 - Digital Image Processing and Feature Extraction D, Lecture, 9.1.2024-20.2.2024
Kurssiasetusten perusteella kurssi on päättynyt 20.02.2024 Etsi kursseja: GIS-E3020
Timetable 2024
Teacher in charge: Petri Rönnholm: petri.ronnholm@aalto.fi
Lectures
Onsite lectures are given in hall k202 (Otakaari 4), but the sessions are recorded enabling a hybrid learning. The recordings are published after the lectures.
Week 2, Tue 9.1., 12:15-14:00, Lecture 1, digital image processing 1, hall k202
Week 2, Thu 11.1., 10:15-12:00, Lecture 2, digital image processing 2, hall k202
Week 3, Tue 16.1., 12:15-14:00, Lecture 3, digital image processing 3, hall k202
Week 3, Thu 18.1., 10:15-12:00, Lecture 4, digital image processing 4, hall k202
Week 4, Tue 23.1., 12:15-14:00, Lecture 5, digital image processing 5, hall k202
Week 4, Thu 25.1., 10:15-12:00, Lecture 6, feature extraction 1, hall k202
Week 5, Tue 30.1., 12:15-14:00, Lecture 7, feature extraction 2, hall k202
Week 5, Thu 1.2., 10:15-12:00, Lecture 8, feature extraction 3, hall k202
Week 6, Tue 6.2., 12-14, Lecture 9, feature extraction 4, hall k202
Week 6, Thu 8.2., 10-12. Lecture 10, Neural Networks, hall k202
Week 7, Tue 13.2., 12:15-16:00, Seminar, hall U135a U7 PWC (Otakaari 1), hopefully as many as possible can physically attend. If you can attend only remotely, take contact with petri.ronnholm@aalto.fi in advance
Week 7, Thu 16.2., 10-12. NO LECTURE
Week 8, Tue 20.2., 12-15 Lecture exam, hall 215
Compulsory assignments
(all compulsory assignments must be passed to get the final grade of the course)
Assignment 1. From an image sensor to an image file, deadline 19.1. (help: Petri Rönnholm petri.ronnholm@aalto.fi, evaluation: Toni Rantanen, toni.rantanen@aalto.fi)
Assignment 2. Sampling of a signal, and interpretation of Fourier spectrums of images, deadline 26.1. (Petri Rönnholm, petri.ronnholm@aalto.fi)
Assignment 3. Digital image processing with Matlab, Deadline 2.2., includes Matlab scripting (Milka Nuikka, milka.nuikka@aalto.fi)
Assignment 4. Seminar, 13.2., 12:15-16:00, hall k202
Assignment 5. Plessley-Harris interest operator, Deadline 22.2., includes Matlab programming (Petri Rönnholm, petri.ronnholm@aalto.fi)
Voluntary assignment
From this assignment, you can get 3 extra points
Assignment 6. Teaching YOLO convolutional neural network to detect teekkari caps with Matlab, pre-written code i.e. includes no programming, deadline 25.2.
To pass the course, you need to have all compulsory assignments accepted and examination passed. The final grade is a combination of assignments and examination grade. The maximum points from compulsory assignments is á 30 points, i.e., total of 150 points (plus voluntary assignment 3 points). This is scaled (no rounding) to range [0, 5]. If the assignment report is returned after the deadline, the maximum point from that assignment is 12 points (equals to grade 2).
The lowest grade to pass the examination is 15/30 points.
The final grade is the rounded average of the assignment and examination grades
E.g.
exam = 2, assignments = 4.5, grade = 3.25 -> 3
exam = 5, assignments = 3, grade = 4