PV253 Seminar of DISA Laboratory

Faculty of Informatics
Spring 2024
Extent and Intensity
0/2. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
Taught in person.
prof. Ing. Pavel Zezula, CSc. (lecturer)
RNDr. Vladimír Míč, Ph.D. (assistant)
doc. RNDr. Jan Sedmidubský, Ph.D. (assistant)
Guaranteed by
prof. Ing. Pavel Zezula, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Tue 10:00–11:50 A218
Students should be interested in cooperation on research projects conducted by the laboratory. The knowledge of English is necessary to study original research papers. Basic experience with programming and data management system implementations is an advantage.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 87 fields of study the course is directly associated with, display
Course objectives
The course objective is a presentation of the state-of-the-art knowledge in research areas of the laboratory. A special emphasis is put on presentations of innovative ideas and proposals by researchers involved in current projects of DISA. The seminar is a source of new knowledge for students and other laboratory members and at the same time it forms a feedback for lecturers (prevalently project researchers). Short student presentations working on bachelor or master theses are also part of the course activities.
Learning outcomes
Student will improve their presentation skills, and will undesrtand selected research results in the domain of the laboratory.
  • The selection of presentations is based on research interests of ongoing projects. At the moment, the topics include:
  • Similarity searching and filtering in multimedia data (mainly images and video);
  • Searching for sub-images;
  • Searching in large collections of biometric data;
  • Similarity models of dynamic biometric characteristics (mainly human movements);
  • Multimodal interpretation of multimedia data;
  • Findability of multimedia data;
  • Scalability for knowledge extraction and searching, etc.
Teaching methods
The seminar consists of presentations and discussions on the state-of-the-art knowledge in topics of interest of the laboratory. The presentations are delivered by both the students and researchers. The specific topics of interest are determined during the first two weeks of each term.
Assessment methods
Regular attendance of the seminar is an assumption. The condition for classification is also an active participation, which can be a software project assignment and/or a presentation on an approved topic.
Language of instruction
Follow-Up Courses
Further Comments
Study Materials
The course is taught each semester.
Teacher's information
The course is also listed under the following terms Autumn 2013, Spring 2014, Autumn 2014, Spring 2015, Autumn 2015, Spring 2016, Autumn 2016, Spring 2017, Autumn 2017, Spring 2018, Autumn 2018, Spring 2019, Autumn 2019, Spring 2020, Autumn 2020, Spring 2021, Autumn 2021, Spring 2022, Autumn 2022, Spring 2023, Autumn 2023.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/spring2024/PV253