199.092 Computer vision and image analysis of art
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020W, VU, 2.0h, 3.0EC
TUWEL

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • Format: Online

Learning outcomes

After successful completion of the course, students are able to...

  • explain key problems in computer image analysis of fine art
  • explain terminology and techniques used by art scholars, in particular connoisseurs
  • make use of computer methods applied to problems in analysis of fine art, based on their theoretical and practical experience gained 

Subject of course

The subject of the course is the application of rigorous image processing, computer vision, pattern recognition, machine learning and artificial intelligence to problems in the history and interpretation of fine art. The lecturer of this course will be David G.Stork and the course will be held via distance learning (live video conference).

Course schedule: Nov 25, 2020 - Jan 31, 2021, Wednesday and Thursday from 6pm - 7pm (CET) via Zoom (Link provided via TUWEL)

Teaching methods

  • Two one-hour online lectures per week (Wednesday and Thursday from 6pm - 7pm (CET))
  • One optional one-hour online "office hour" (to answer student questions, help choose programming projects, prepare for exam, etc.)
  • Email correspondence

Mode of examination

Immanent

Additional information

This is a visiting professor course of the Vienna PhD School of Informatics.

First Lecture 25.11.2020 via ZOOM (Link provided via TUWEL)

ECTS Breakdown:

  • Online lecture:  24 hr
  • Individual project, including development of code (Matlab, or language of choice):  28 hr
  • Preparation for practical assignments:  28.25 hr
  • Presentation of project to class:  .25 hr
  • Exam preparation:  30 hr
  • Final exam:  2 hr
  • Total:  112.5


Lecturers

Institute

Examination modalities

  • Weekly homework exercises (verbal explanations, mathematical problems, one or two simple coding exercises):  40% of overall grad
  • Individual programming project (15-minute presentation to class plus five-page writeup with code and images):  40% of overall grade
  • Final exam:  20% of overall grade
  • Examination modalities: Two-hour "in-class" final exam

Course registration

Begin End Deregistration end
01.09.2020 00:00 23.10.2020 23:59

Registration modalities

Please register in TISS.

Curricula

Study CodeObligationSemesterPrecon.Info
PhD Vienna PhD School of Informatics Not specified

Literature

  • David G. Stork, Pixels & paintings:  Foundations of computer-assisted connoisseurship (Wiley) [.pdf draft manuscript distributed to students]
  • Scholarly papers from SPIE, IEEE, IS&TACM, AAAICAA (College Art Association), conferences such as CVPR, ECCVICCV, IP4AI, etc.
  • Online lectures from others (e.g., Youtube, National Gallery London, National Gallery Washington, Museum of Modern Art, ...)

Language

English