A course in computer science focusing on basic theory, models, and methods for computer vision, image analysis and image processing.
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Content and learning outcomes
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.
Literature and preparations
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.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- 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.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
- 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.
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
Main field of study
The course has replaced DD2422 Image Analysis and Computer Vision.
In this course, the EECS code of honor applies, see: