Učni načrt predmeta

Predmet:
Računalniški vid
Course:
Computer Vision
Študijski program in stopnja /
Study programme and level
Študijska smer /
Study field
Letnik /
Academic year
Semester /
Semester
Senzorske tehnologije, 3. stopnja / 1 1
Sensor Technologies, 3rd cycle / 1 1
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
ST3-554
Predavanja
Lectures
Seminar
Seminar
Vaje
Tutorial
Klinične vaje
work
Druge oblike
študija
Samost. delo
Individ. work
ECTS
15 15 15 105 5

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:
prof. dr. Aleš Ude
Sodelavci / Lecturers:
Jeziki / Languages:
Predavanja / Lectures:
Slovenski ali angleški / Slovene or English
Vaje / Tutorial:
Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:
Prerequisits:
Vsebina:
Content (Syllabus outline):
Temeljna literatura in viri / Readings:
Cilji in kompetence:
Objectives and competences:
Predvideni študijski rezultati:
Intendeded learning outcomes:
Metode poučevanja in učenja:
Learning and teaching methods:
Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Seminarska naloga s predstavitvijo in zagovorom rešitve izbranega problema iz študentovega raziskovalnega dela
60 %
Seminar work with presentation and defence of the proposed solution for the selected problem from student’s research work
Ustni izpit
40 %
Oral exam
Reference nosilca / Lecturer's references:
1. M. Mavsar, B. Ridge, R. Pahič, J. Morimoto, and A. Ude (2022) Simulation-aided handover prediction from video using recurrent image-to-motion networks, IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13, doi: 10.1109/TNNLS.2022.3175720.
2. D. Schiebener, J. Morimoto, T. Asfour and A. Ude (2013) Integrating visual perception and manipulation for autonomous learning of object representations, Adaptive Behavior, vol. 21, no. 5, pp. 328-345.
3. A. Ude, D. Schiebener, N. Sugimoto, and J. Morimoto (2012) Integrating surface-based hypotheses and manipulation for autonomous segmentation and learning of object representations, IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, Minnesota, pp. 1709-1715 (pdf file). Finalist for Best Cognitive Robotics Paper award.
4. D. Omrčen and A. Ude (2010) Redundancy control of a humanoid head for foveation and threedimensional object tracking: A virtual mechanism approach, Advanced Robotics, vol. 24, no. 15, pp. 2171-2197.
5. A. Ude, D. Omrčen, and G. Cheng (2008) Making object learning and recognition an active process, International Journal of Humanoid Robotics, vol. 5, no. 2, pp. 267-286.