FU:APDMB041 Intelligent Data Processing - Course Information
APDMB041 Intelligent Data ProcessingInstitute of physics in Opava
- Extent and Intensity
- 2/2/0. 6 credit(s). Type of Completion: zk (examination).
- doc. Ing. Petr Čermák, Ph.D. (lecturer)
- Guaranteed by
- doc. Ing. Petr Čermák, Ph.D.
Institute of physics in Opava
- ( FAKULTA ( FU ) && TYP_STUDIA ( B ))
- 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
- Physical Diagnostic Methods (programme FU, APFYZB)
- Course objectives
- Students will get acquainted with selected methods of intelligent data processing, including decision-making over these data.
- Learning outcomes
- Upon completion of the course, the student will be able to:
describe and explain sound processing and sound processing methods with a focus on speech processing;
describe and explain Markov's hidden chains, applications in voice recognition;
describe the basic chain of image processing;
describe the methods of segmentation, detection of segments and their classification;
describe and explain the detection of key points and uses;
describe the PCA dimension reduction method; describe and explain decision-making methods and expert systems.
- 1. Audio processing, audio and speach recognition
- 2. Markov hidden chains, application in voice recognition
- 3. Image recognition, basic processing pipeline
- 4. Selected methods of segmentation
- 5. Analysis, detection and classification of image segments
- 6. Detection of key points in the image
- 7. Examples of segmentation, classification and detection of significant points in medical images (CT, MR, US)
- 8. Multidimensional data processing, PCA, dimension reduction
- 9. Multi-criteria decision-making over multidimensional data
- 10. Expert systems
- 11. Examples of data analysis for environmental monitoring.
- required literature
- LEVER, J., KRZYWINSKI, M., ALTMAN, N., Points of Significance . Principal component analysis, https://doi.org/10.1038/nmeth.4346.
- NOUZA, J.: Pokročilé metody rozpoznávání řeči. 2016 [cit 2018-01-13]. Dostupné online http://itakura.ite.tul.cz/jan/PMR/.
- SZELISKI, Richard. Computer Vision: Algorithms and Applications. Berlin, 2010.
- MAŘÍK a kol. Umělá inteligence I, II. Praha, 2001.
- ZELINKA, I. Evoluční výpočetní techniky, principy a aplikace. Praha, 2008. ISBN 978-80-7300-218-3.
- DOUGHERTY, G. Digital Image Processing for Medical Applications. Oxford, 2009. ISBN 978-0521860857.
- Teaching methods
- Forms of teaching will be as follows:
1. theoretical preparation (lectures);
2. laboratory exercises (processing of data).
- Assessment methods
- Active participation in seminars,demonstrating knowledge of the issue of the study subject on the oral examination.
- Language of instruction
- Further Comments
- The course is taught annually.
The course is taught: every week.
- Enrolment Statistics (summer 2022, recent)
- Permalink: https://is.slu.cz/course/fu/summer2022/APDMB041