Machine Vision

Understanding Machine Vision technology applications; Characteristics of Systems; Vision System Elements, Sensors, and data acquisition; Biological-Based Optical Sensors and Transducers; Machine Vision Importance for Real Mechatronic Applications and Automation; Machine Vision Industry; Machine Vision Concepts and algorithms; Image Acquisition; Image Conversion; Optical Information Processing and Pattern Recognition; Real-Time Feature Extraction and Image Recognition; Feature Selection and Planning for Visual Servoing; Image Processing and Decision-Making; Visual Methods for Monitoring and Detecting; Visual Guidance for Robots; Three-Dimensional Machine Vision Techniques; Evaluating Machine Vision Applications; Application Analysis and Implementation; Alternatives to Machine Vision and trends. Use of AI and Machine Learning at Machine Vision problem solving.

NB! This course will take place in autumn semester 2025/2026 which starts on 1st of September and ends on 25th of January (you can find that information under Start date section). TalTech's timetables for Autumn semester 2025 will be published at the end of June via tunniplaan.taltech.ee. Switch the page to English and use "Search" and "Open detailed search" to find your course. NB! Some courses are taught by several lecturers during the same semester. Make sure that the course name and lecturer/teacher infromation of your course match with the information given in the Course Catalogue.

Knows and orients in basic principles and structures of high-tech machine vision systems and their use in mechatronics, robotics and production systems. Knows the main techniques and methods for machine vision applications and their usability in technology applications. Knows and is able to use design and analysis concepts for developing practical machine vision systems for solving basic related technical problems. Knows and is able to implement machine vision software to automate technology processes on the base of visual information.

Final assessment can consist of one test/assignment or several smaller assignments completed during the whole course. After declaring a course the student can re-sit the exam/assessment once. Assessment can be graded or non-graded. For specific information about the assessment process please get in touch with the contact person of this course. For specific information about grade transfer please contact your home university

MATLAB and Phyton programming, calculus. NB! Presence on campus 2 weeks (dates to be specified).

• Loengukonspekt (lecture slides and exrecise materials)

lectures, practices, exercises