Pattern Recognition

Higher education teachers: Dobrišek Simon
Collaborators: Pavešić Nikola
Credits: 5
Subject code: 64839



Subject description

Prerequisits:

  • Enrolment in the corresponding year of the study programme.

Content (Syllabus outline):

  • Introduction: definitions, pattern representations, pattern recognition by classification and analysis, applications of pattern recognition in economy, traffics, medicine, robotics, banking, forensics, man-machine communication, etc.
  • Pattern pre-processing: restoration, enhancement, normalization.
  • Pattern segmentation: basic idea,
  • image segmentation, and
  • auditory signals segmentation.
  • Features: generation of features by heuristic and mathematical methods.
  • Analysis of learning sets: pattern similarity measures, pattern clustering test, crisp and fuzzy clustering, clustering techniques, deep learning of generative models.
  • Pattern classification: classification of feature vectors by matching, decision, inference, and artificial neural networks; classification of sequences by dynamic programming and Hidden Markov Models; classification by graph matching; classification of statistically dependent samples.
  • Combining and fusing classifiers.

Objectives and competences:

To acquaint students the advanced mathematical and computational approaches to pattern recognition by classification and analysis.

Intended learning outcomes:

After completion of the course the student will be able to demonstrate knowledge and understanding of:

  • developing systems based on recognition of external signals,
  • modelling rational capabilities of human beings (e.g. perception and cognition of the environment, learning),
  • state-of-the-art methods for pattern segmentation, feature extraction, clustering and classification.

During the course the student will gain and improve transferable skills such as:

  • use of information technology: the use of development tools (OpenCV,WEKA), programming environments (Matlab, GCC, Netbeans), programming languages (C++,Java);
  • problem solving: problem analysis, algorithm design, implementation and testing of a program; and
  • group work: the organisation and management of groups, active participation in groups.

Learning and teaching methods:

  • lectures,
  • seminar projects.





Study materials

  1. N. Pavešić: Razpoznavanje vzorcev : uvod v analizo in razumevanje vidnih in slušnih signalov, 3., popravljena in dopolnjena izdaja, Založba FE in FRI, 2012. 2 zv. ([XVI], 707 str.), ilustr. ISBN 978-961-243-201-0. [COBISS.SI-ID 260256256]
  2. S. Theodoridis, K. Koutroumbas, Pattern Recognition, Fourth Edition, Academic Press, 2009 [COBISS.SI-ID 1497508]
  3. C. M. Bishop, Pattern recognition and machine learning, New York : Springer, 2009 [COBISS.SI-ID 7988308]