Teacher in charge: Petri Rönnholm (petri.ronnholm@aalto.fi)

Lectures

Lectures are mainly contact teaching, but the sessions are recorded enabling hybrid learning. In the week 6, we do not have contact teaching, but only pre-recorded video lectures.

Week 2, Tue 10.1., 12:15-14:00, Lecture 1, digital image processing 1, contact teaching (hall k202), recorded

Week 2, Thu 12.1., 10:15-12:00, Lecture 2, digital image processing 2, contact teaching (hall k202), recorded

Week 3, Tue 17.1., 12:15-14:00, Lecture 3, digital image processing 3, contact teaching (hall k202), recorded

Week 3, Thu 19.1., 10:15-12:00, Lecture 4, digital image processing 4, contact teaching (hall k202), recorded

Week 4, Tue 24.1., 12:15-14:00, Lecture 5, digital image processing 5, contact teaching (hall k202), recorded

Week 4, Thu 26.1., 10:15-12:00, Lecture 6, feature extraction 1, contact teaching (hall k202), recorded

Week 5, Tue 31.1., 12:15-14:00, Lecture 7, feature extraction 2, contact teaching (hall k202), recorded

Week 5, Thu 2.2., 10:15-12:00, Lecture 8, feature extraction 3, contact teaching (hall k202), recorded

Week 6, Tue 7.2., 12-14, Lecture 9, feature extraction 4, only a pre-recorded video lecture

Week 6, Thu 9.2., 10-12. Lecture 10, Neural Networks, only a pre-recorded video lecture

Week 7, Tue 14.2., 12:15-16:00, Seminar, hall U356 (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 21.2., 12-15 Examination (Open book examination in MyCourses)

 

Compulsory assignments

(all compulsory assignments must be passed to get the final grade of the course)

Assignment 1. From image sensor to an image file, deadline 20.1. (Toni Rantanen, toni.rantanen@aalto.fi)

Assignment 2. Sampling of a signal, and interpretation of Fourier spectrums of images, deadline 27.1. (Petri Rönnholm, petri.ronnholm@aalto.fi)

Assignment 3. Digital image processing with Matlab, Deadline 3.2., includes Matlab scripting (Petri Rönnholm, petri.ronnholm@aalto.fi)

Assignment 4. Seminar, 14.2., hall U356 (Otakaari 1)

Assignment 5. Plessley-Harris interest operator, Deadline 23.2., includes Matlab programming (Petri Rönnholm, petri.ronnholm@aalto.fi)

 

Voluntary assignment

From this assignment, you can get 10 extra points

Assignment 6. Teaching YOLO convolutional neural network to detect teekkari caps with Matlab, pre-written code i.e. includes no programming, deadline 26.2.

 

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 10 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.

examination = 2, assignments = 4.5, grade = 3.25 -> 3

examination = 5, assignments = 3, grade = 4


Last modified: Friday, 6 January 2023, 2:27 PM