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Vår 2021

FYS-2010 Image Analysis - 10 stp


The course is administrated by

Faculty of Science and Technology

Type of course

The course is available as a singular course. The course is also available to exchange students and Fulbright students.

Course contents

The course introduces fundamental topics in digital image analysis, comprising both mathematical operations on images (image processing) and their use in image understanding and interpretation (computer vision). The course covers mathematical characterization of discrete images, sampling, reconstruction and important image transforms. It teaches image filtering in the spatial and frequency domain covering image enhancements, noise removal, and detection of edge, point and corner features that can be used in vision tasks. It also covers algorithms for object detection and extraction, including thresholding, segmentation and classification. The course describes the evolution from image filtering by convolution with static operators to adaptive processing with convolutional neural networks (CNNs) that learn their filters from data. It gives an introduction to deep learning and training of CNNs for image analysis tasks. The course emphasizes practical exercises. It is relevant for further studies in various fields, such as machine learning, remote sensing (earth observation, space physics, optics, microwaves and ultrasound), automation, robotics, and energy data analytics.

Fundamental knowledge of programming is presupposed.


Application deadline

Applicants from Nordic countries: 1 December

Exchange students and Fulbright students: 1 October


Admission requirements

Admission requirements are generell studiekompetanse + SIVING.

Local admission, application code 9391 - singular courses in engineering sciences. The course is also available to exchange students and Fulbright students.


Objective of the course

Knowledge - The student can:

Skills - The student can:

General competence - The student can:


Language of instruction

The language of instruction is English and all of the syllabus material is in English. Examination questions will be given in English, but may be answered either in English or a Scandinavian language.

Teaching methods

Lectures: 40 hours

Exercises: 40 hours


Assessment

Portfolio assessment:

All components in the portfolio are assessed as a whole and one combined grade is given.

Assessment scale: Letter grades A-F. A is the highest grade and F is failed.

Postponed examination (sections 17 and 21): Students with valid grounds for absence will be offered a postponed examination. Postponed take-home examination is arranged during the semester if possible, otherwise early in the following semester. Postponed written examination is held early in the following semester.

See indicated sections in Regulations for examinations at the UiT The arctic university of Norway for more information.


Course overlap

FYS-262 Digital image processing 9 stp
Lectures Spring 2021
Lectures f.aman. Stian Normann Anfinsen
post.doc. Martine Mostervik Espeseth