DD2423 Image Analysis and Computer Vision 7.5 credits

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A course in computer science focusing on basic theory, models, and methods for computer vision, image analysis and image processing.

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Course information

Content and learning outcomes

Course contents *

Overview about aims and methods for image analysis, image processing and computer vision. Orientation about biological seeing and visual perception. Properties of the perspective image formation.

Basic image analysis: signal theoretical methods, filtering, image enhancement, image reconstruction, segmentation, classification, representation.

Basic computer vision: multiscale representation, detection of edges and other distinctive features. Stereo and multi-camera systems. Object recognition, morphology.

Intended learning outcomes *

After completing the course with a passing grade the student should be able to:
• identify basic concepts, terminology, models and methods in computer vision and image processing
• develop and evaluate a number of basic methods in computer vision and image processing systematically
• choose and apply methods for processing of image data related to image filtrering, image enhancement, segmentation, classification and representation,
• account for basic methods in computer vision as multiscale representation, detection of edges and other distinctive features, stereo, movement and object recognition to
• later as a working professional be able to decide how basic possibilities and limitations influence the choice of methods in image processing and computer vision for specific applications
• independently be able to implement, analyse and evaluate simple methods for computer vision and image processing
• be able to read and apply professional literature in the area.

Course Disposition

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Literature and preparations

Specific prerequisites *

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Recommended prerequisites

The courses in the basic block on mathematics, computer science and numerical analysis on the D-, E- or F-programme. One more course on signal processing and/or numerical analysis can be recommended. We recommend the students to read the course during the fourth year because it uses prerequisites from a relative wide spectrum of applied mathematics and computer science.

Equipment

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Literature

The reading list is announced on the course page.

Examination and completion

Grading scale *

A, B, C, D, E, FX, F

Examination *

  • LAB1 - Laboratory Work, 4.0 credits, Grading scale: A, B, C, D, E, FX, F
  • TEN1 - Examination, 3.5 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

In agreement with KTH´s coordinator for disabilities, it is the examiner who decides to adapt an examination for students in possess of a valid medical certificate. The examiner may permit other examination forms at the re-examination of few students.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Examiner

Mårten Björkman

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web DD2423

Offered by

EECS/Intelligent Systems

Main field of study *

Computer Science and Engineering

Education cycle *

Second cycle

Add-on studies

No information inserted

Contact

Mårten Björkman, e-post: celle@kth.se

Ethical approach *

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Supplementary information

The course has replaced DD2422 Image Analysis and Computer Vision.

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex