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# Details for Course FMA171F Image Analysis

General
• FMA171F
• Temporary
Course Name
• Image Analysis
Course Extent
• 7.5
Type of Instruction
• Course given jointly for second and third cycle
• 7151 (Centre of Mathematical Sciences / Mathematics)
•  -01-31
• FN1/Anders Gustafsson

## Current Established Course Syllabus

General
• English
• Every autumn semester
Aim
• The aim of the course is to give necessary knowledge of digital image analysis for further research within the area and to be able to use digital image analysis within other research areas such as computer graphics, image coding, video coding and industrial image processing problems. The aim is also to prepare the student for further studies in e.g. computer vision, multispectral image analysis and statistical image analysis.
Contents
• Basic mathematical concepts: Image transforms, DFT (Discrete Fourier Transform), FFT (Fast Fourier Transform).
Image enhancement: Grey level transforms, filtering.
Image restoration: Filterings, inverse methods.
Scale space theory: Continuous versus discrete theory, interpolation.
Extraction of special features: Filtering, edge and corner detection.
Segmentation: graph-methods, active contours, mathematical morphology.
Bayesian image handling: MAP(Maximum Aposteriori) estimations, simulation.
Pattern recognition: Classification, SVM (Support Vector Machines), PCA (Principal Component Analysis), learning.
Registration.
Machine Learning: Training, testing, generalization, hypothesis spaces.

Knowledge and Understanding
• For a passing grade the doctoral student must
• be able to explain clearly, and to independently use, basic mathematical concepts in image analysis, in particular regarding transform theory (in space as well as in the frequency domain), image enhancement methods, image compression and pattern recognition.

be able to describe and give an informal explanation of the mathematical theory behind some central image processing algorithms (both deterministic and stochastic).

have an understanding of the statistical principles used in machine learning
Competences and Skills
• For a passing grade the doctoral student must
• in an engineering manner be able to use computer packages to solve problems in image analysis.

be able to show good capability to independently identify problems which can be solved with methods from image analysis, and be able to choose an appropriate method.

be able to independently apply basic methods in image processing to problems which are relevant in industrial applications or research.

with proper terminology, in a well structured way and with clear logic be able to explain the solution to a problem in image analysis.
Judgement and Approach
• For a passing grade the doctoral student must
Types of Instruction
• Lectures
• Laboratory exercises
• Exercises
Examination Formats
• Written assignments
• Failed, pass
Assumed Prior Knowledge
Selection Criteria
Literature
• Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, 2010. ISBN 9781848829343.
• It is possible to pass the course without owning the book, using material available through the course home page.
Further Information
Course code
• FMA171F
•  -01-31
• FN1/Anders Gustafsson

## All Established Course Syllabi

1 course syllabus.

Valid from First hand in Second hand in Established
Autumn 2013 2013‑10‑11 16:19:33 2014‑01‑27 14:12:49 2014‑01‑31

## Current or Upcoming Published Course Occasion

No matching course occasion was found.

## All Published Course Occasions

1 course occasion.

Course syllabus valid from Start Date End Date Published
Autumn 2013 2015‑09‑01 2015‑11‑01