TSBB36 · 6 credits · Basic level (G2F) · Elektroteknik
Multidimensional Signal and Image Analysis
Active · next start HT 2026
44%
passed the latest ordinary exam
TEN1 · 14 Jan 2026 · 16 students
57%
passed the ordinary exams, all sittings over the last 5 years
TEN1 · 42 students
83%
Harder than 83% of courses at basic level
Compared with 874 courses at the same level, ordinary exams over the last three academic years. 42 students.
Part of 2 programmes
Exam
TEN1 Written Examination
- 5
- 4
- 3
- U
Ordinary exams
57% passed · 42 students
- 44%16
- 65%26
Re-exams
77% passed · 13 students
- 88%8
- 60%5
- Pass the re-exam: 77% (13 students), compared with 57% (42 students) on the ordinary exam
- Get 4 or 5: 20% (55 students)
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%2
- 100%1
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB1 Laboratory Work
- G
Main rounds
100% passed · 45 students
- 100%19
- 100%26
Later rounds
No sittings with at least 5 students.
About the course
Aim
An objective of the course is to provide a solid theoretical foundation for further studies and applications in 3D computer vision and in machine learning with images. Passing the course means that the student is able to use concepts and methods in signal and image analysis that are based on linear algebra. The student is then able to:
Goal 1: Explain and use homogeneous coordinates for geometric calculations in two and three dimensions. This includes points and lines in two dimensions, points, planes, and lines in three dimensions, homographies, camera projections, and epipolar geometry.Goal 2: Explain and use least squares techniques to define estimation problems for different geometric objects. This includes triangulation, image filtering, and rigid transformations.Goal 3: Explain and use bases, dual bases, and subspace bases to perform calculations for signal analysis.Goal 4: Explain and use linear signal representations on practical problems, including: image filtering, image blending, and feature maps.
Content
Signal spaces and signal bases, dual bases. Least squares problem, normalized convolution. Eigenvalue and singular value analysis. Principal component analysis. Feature maps. Projective spaces, homogeneous coordinates, homographies, camera projections, epipolar geometry. Representation and estimation of various types of geometric objects.
- Examiner
- Mårten Wadenbäck
- Scheduled hours
- 0 h
- Main field of study
- Elektroteknik