DD2427 Image Based Recognition and Classification 6.0 credits

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Please note

The information on this page is based on a course syllabus that is not yet valid.

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.

  • Educational level

    Second cycle
  • Academic level (A-D)

    D
  • Subject area

  • Grade scale

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

Course offerings

Spring 16 bik16 for programme students

Spring 17 bik17 for programme students

Spring 17 SAP for single courses students - To application

  • Periods

    Spring 17 P4 (6.0 credits)

  • Application code

    61052

  • Start date

    2017 week: 12

  • End date

    2017 week: 23

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Number of lectures

    12 (preliminary)

  • Number of exercises

    2 (preliminary)

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places

    No limitation

  • Course responsible

    Josephine Sullivan <sullivan@kth.se>

  • Target group

    Single course students

  • 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.

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.

Eligibility

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

Literature

Föreläsningsanteckningar, delas ut vid kursstart.

Examination

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

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

Offered by

CSC/Computer Science

Contact

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

Examiner

Josephine Sullivan <sullivan@kth.se>

Add-on studies

Discuss with course leader.

Version

Course syllabus valid from: Autumn 16.
Examination information valid from: Autumn 07.