DD2427 Image Based Recognition and Classification 6.0 credits

Bildbaserad igenkänning och klassificering

Please note

This course has been cancelled.

A course in computer science focusing on basic theory, models, and methods for classification of data with special emphasis on object recognition in digital images.

  • Education cycle

    Second cycle
  • Main field of study

    Computer Science and Engineering
  • Grading scale

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

Last planned examination: spring 19.

At present this course is not scheduled to be offered.

Intended learning outcomes

After the course you should be able to:

  • identify basic notions, terminology, theories models and methods for classification of data,
  • develop and systematically evaluate a number of basic methods for classification of data,
  • experimentally evaluate algorithms for classification and recognition of objects in digital gray value images,
  • choose appropriate method in order to automatically solve a given classification problem,
  • know about theories of how the brain processes visual information for classification,

in order to

  • be able to solve general problems of data representation and classification,
  • be able to implement, analyze and evaluate simple systems for automatic classification of images,
  • obtain a broad base of knowledge in order to be able to acquire information about and read literature in the field.

Course main content

  • Representation and feature extraction in digital images
  • principles of recognition and classification, Bayesian decisions
  • discriminant functions, neural networks, support vector machines
  • learning, optimization of classifiers
  • overview of recognition in biological systems
  • examples of recognition: handwritten text, faces, objects.


Single course students:

SF1604 Linear Algebra, SF1625 Calculus in one variable, SF1626 Calculus in Several Variables, DD1337 Programming or corresponding courses

Recommended prerequisites

SF1901 Probability Theory and Statistics,  DD2431 Machine Learning


Föreläsningsanteckningar, delas ut vid kursstart.


  • INL1 - Assignment, 1.5, grading scale: P, F
  • LAB1 - Laboratory Work, 1.5, grading scale: P, F
  • TEN1 - Examination, 3.0, grading 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

Laboratory assignments (LAB1; 1,5 university credits)
Hand in exercise (INL1; 1,5 university credits)
Examination (TEN1; 3 university credits )

Offered by

CSC/Robotics, Perception and Learning


Josephine Sullivan, tel: 790 6136, e-post: sullivan@kth.se


Josephine Sullivan <sullivan@kth.se>

Add-on studies

Discuss with course leader.


Course syllabus valid from: Autumn 2016.
Examination information valid from: Autumn 2007.