Intelligent Systems in Automation

Higher education teachers: Dobrišek Simon

Subject description

Prerequisits:

• Elementary knowledge of high school mathematics and computer programming.

Content (Syllabus outline):

• Introduction to pattern recognition and artificial intelligence: basic concepts and terminology.
• Processing and recognition of visual patterns: image acquisition, image segmentation, shape and texture features, automatic learning, and object recognition.
• Automatic visual detection and recognition of persons in surveilled areas. Methods of visual detection and recognition of faces and gaits in images.
• Processing and recognition of auditory patterns: speech signal acquisition and segmentation, speech features (energy, cepstral coefficients and dynamic features), and automatic learning for isolated commands recognition.
• Speech synthesis: acoustical modelling of speech, main methods of speech synthesis, learning speech synthesis from speech recordings.
• Speech-based man-machine communication: system components for speech-based man-machine communication, speech recognition system, speech synthesis system, dialog system.

Objectives and competences:

The aim of this course is to acquaint the student with the basic concepts and components of artificial intelligent systems in automation: computer vision, speech recognition and synthesis, and modern modes of man-machine communication.

Intended learning outcomes:

• After completing this course the student will be able to demonstrate knowledge and understanding of: building intelligent systems based on the use of the methods of visual and auditory patterns recognition; general usage of computer methods of image and speech processing in automation; and building intelligent user interfaces supporting natural man-machine communication.
• During the course the student will gain and improve transferable skills such as: searching for and using professional information sources in the field of artificial intelligence, computer vision, and speech technologies; use of information technology: the use of open source development tools (OpenCV,WEKA), programming environments (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 exercises,
• laboratory exercises,
• coursework.

Study materials

Readings:

1. R. C. Gonzalez, R. E. Woods, S. L. Eddins: Digital Image Processing Using MATLAB , 2. izdaja, Gatesmark Publishing, 2009.
2. J. C. Russ: The Image Processing Handbook, 6. izdaja, CRC, 2011.
3. R. Pieraccini: The Voice in the Machine: Building Computers That Understand Speech, MIT Press , 2012.
4. P. Taylor: Text-to-Speech Synthesis, Cambridge University Press, 2009.