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.

  • Educational level

    Second cycle
  • Academic level (A-D)

    D
  • Subject area

    Computer Science and Engineering
  • Grade scale

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

Course offerings

Autumn 17 SAP for single courses students - To application

  • Periods

    Autumn 17 P2 (7.5 credits)

  • Application code

    10001

  • Start date

    30/10/2017

  • End date

    2018 week: 1

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Number of lectures

  • Number of exercises

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places

    No limitation

  • Course responsible

    Mårten Björkman <celle@kth.se>

  • Teacher

    Tony Lindeberg <tony@kth.se>

  • Target group

    Single course students.

  • Part of programme

  • Application

    Apply for this course at antagning.se through this application link.
    Please note that you need to log in at antagning.se to finalize your application.

Autumn 17 bildat17 for programme students

Autumn 16 SAP for single courses students

  • Periods

    Autumn 16 P2 (7.5 credits)

  • Application code

    50002

  • Start date

    2016 week: 45

  • End date

    2017 week: 3

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Number of lectures

  • Number of exercises

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places

    No limitation

  • Course responsible

    Mårten Björkman <celle@kth.se>

  • Teacher

    Tony Lindeberg <tony@kth.se>

  • Target group

    Single course students.

Autumn 16 bildat16 for programme students

Intended learning outcomes

After completing the course you will be able to:

* identify basic concepts, terminology, theories, models and methods in the field of computer vision, image analysis and image processing,

* describe known principles of human visual system,

* develop and systematically test different basic methods of computer vision, image analysis and image processing,

* experimentally evaluate different image analysis algorithms and summarize the results,

* choose appropriate image processing methods for image filtering, image restauration, image reconstruction, segmentation, classification and representation,

* describe basic methods of computer vision related to multi-scale representation, edge detection and detection of other primitives, stereo, motion and object recognition,

* build a toolbox for image processing consisting of methods for grey-level transformations, image filtering functions and methods for edge and corner detection,

* suggest a design of a computer vision system for a specific problem

in order to

* get acquainted with basic possibilities and constraints of computer vision, image processing and image analysis and therefore assess which problems can be solved in the field of robotics, medical and industrial image processing, processing of satelite images and similar,

* be able to implement, analyse and evaluate simple systems for automatic image processing and computer vision,

* have a broad knowledge base so to easily read the related literature.

Course main content

Overview of goals and methods of image analysis and computer vision. Introduction to biological vision and visual perception. Perspective projection.

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

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

Eligibility

Single course students: 90 university credits including 45 university credits in Mathematics and/or Information Technology and the courses SF1604 Linear algebra, SF1625 Calculus in one variable, SF1626 Calculus in several variables, SF1901 Probability theory and statistics, DD1337 Programming and DD1338 Algorithms and Data Structures or equivalent.

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.

Literature

R. C. Gonzalez and R. E. Woods: Digital Image Processing Prentice Hall, 3rd edition, 2008.

Examination

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

In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.

Requirements for final grade

LAB1 - Laboratory Work, 4 cr
TEN1 - Examination, 3,5 cr

Offered by

CSC/Robotics, Perception and Learning

Contact

Mårten Björkman, e-post: celle@kth.se

Examiner

Mårten Björkman <celle@kth.se>

Supplementary information

The course has replaced DD2422 Image Analysis and Computer Vision.

Version

Course syllabus valid from: Autumn 2015.
Examination information valid from: Spring 2010.