Topical research themes I

There are no prerequisites.

The course is lectured by (younger) professors who present novelties from practically oriented research work. Currently uncovered topics interesting due to recent technological breakthroughs or their applicative value are presented. The lecturer and specific contents of the course are determined annually according to the propositions, programme needs, and latest research trends.

T. Hastie, R. Tibshirani, J. Friedman: The elements of statistical learning, 2nd edition. Springer, 2009.
J. L. Hennessy, D. A. Patterson, Computer Architecture, 5th edition: A Quantitative Approach. Morgan Kaufmann, 2011.
Dodatna literatura se predpiše vsako leto posebej glede na vsebino in predloge izbranega predavatelja.
Additional literature is given annually, with respect to the current topic of the course.

The goal of the course is a transfer of recent research results into the curriculum. Students will be introduced to novel technological breakthroughs as well as practical implementations of new methods and technologies in the field of computer and information science.

After completing this course a student will:

• Be familiar with the field of study from the practical point of view, and recent new methods
  and concepts.

• Know current practically oriented approaches and techniques from the specific field of computer and
  information science in.

• Understand the advantages of the chosen approaches in computer and information science in
  solving specific practical tasks.

• Know how to solve complex problems, and design complex systems.

Lectures, lab work.

Continuing (homework, midterm exams, project work)
Final (written and oral exam)
grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

ČEHOVIN, Luka, KRISTAN, Matej, LEONARDIS, Aleš. Robust visual tracking using an adaptive coupled-layer visual model. IEEE transactions on pattern analysis and machine intelligence, ISSN 0162-8828. [Print ed.], Apr. 2012, vol. 35, no. 4, str. 941-953. [COBISS-SI-ID 9431124]
SULIĆ KENK, Vildana, MANDELJC, Rok, KOVAČIČ, Stanislav, KRISTAN, Matej, HAJDINJAK, Melita, PERŠ, Janez. Visual re-identification across large, distributed camera networks. Image and vision computing, ISSN 0262-8856. [Print ed.], Feb. 2015, vol. 34, str. 11-26. [COBISS-SI-ID 10896980]
KRISTAN, Matej, LEONARDIS, Aleš, SKOČAJ, Danijel. Multivariate online kernel density estimation with Gaussian kernels. Pattern recognition, ISSN 0031-3203. [Print ed.], 2011, vol. 44, no. 10/11, str. 2630-2642. [COBISS-SI-ID 8289876]
KRISTAN, Matej, KOVAČIČ, Stanislav, LEONARDIS, Aleš, PERŠ, Janez. A two-stage dynamic model for visual tracking. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, ISSN 1083-4419. [Print ed.], Dec. 2010, vol. 40, no. 6, str. 1505-1520. [COBISS-SI-ID 7709524]
KRISTAN, Matej, PERŠ, Janez, PERŠE, Matej, KOVAČIČ, Stanislav. Closed-world tracking of multiple interacting targets for indoor-sports applications. Computer vision and image understanding, ISSN 1077-3142. [Print ed.], May 2009, vol. 113, no. 5, str. 598-611. [COBISS-SI-ID 6401620]