IN5520 – Digital Image Analysis
Changes in the course due to coronavirus
Autumn 2020 we plan for teaching and examinations to be conducted as described in the course description and on semester pages. However, changes may occur due to the corona situation. You will receive notifications about any changes at the semester page and/or in Canvas.
Spring 2020: Teaching and examinations was digitilized. See changes and common guidelines for exams at the MN faculty spring 2020.
The course covers methods for analysis of digital images, segmentation, and object description. Central topics are feature extraction and classification of objects in images.
After this course you´ll:
- have good knowledge on methods for texture analysis and the assumptions behind them.
- have good knowledge of object shape representation and description.
- understand the theory and the linear algebra behind central methods for supervised classification of images, in particular Gaussian classifiers.
- understand the concept of curse-of-dimensionality and can use the theory in practice to carefully choose a subset of features and control the complexity of the classifier robustly.
- know the theory behind central methods for feature selection and linear feature transforms.
Admission to the course
Students admitted at UiO must apply for courses in Studentweb. Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.
If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures for international applicants.
Recommended previous knowledge
INF1010 – Object oriented programming (continued)/INF1100 – Introduction to programming with scientific applications (continued), IN1000 – Introduction to Object-oriented Programming, IN1900 – Introduction to Programming with Scientific Applications, INF2310 – Digital bildebehandling, MAT1110 – Calculus and Linear Algebra
- 10 credits overlap with IN9520 – Digital Image Analysis.
- 10 credits overlap with INF4300 – Digital image analysis (continued).
- 10 credits overlap with INF9305 – Digital Image Analysis (continued).
- 5 credits overlap with INF9305 – Digital Image Analysis (continued).
2 hours of lectures and 2 hours of exercises each week. You must pass all mandatory assignments before the exam.
Oral or written exam (4 hours) dependent on the number of student.
All mandatory assignments must be passed prior to the exam.
It will also be counted as one of your three attempts to sit the exam for this course, if you sit the exam for one of the following courses: INF4300 – Digital image analysis (continued), INF9305 – Digital Image Analysis (continued), IN9520 – Digital Image Analysis
Examination support material
Language of examination
You may write your examination paper in Norwegian, Swedish, Danish or English.
Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.
Resit an examination
Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester. Re-scheduled examinations are not offered to students who withdraw during, or did not pass the original examination.