Code: E371100 Machine perception and image analysis
Lecturer: prof. Ing. Václav Hlaváè CSc. Weekly load: 2P+2C Completion: A, EX
Department: 12110 Credits: 5 Semester: W
Description:
We will introduce students to machine perception, a necessary prerequisite for building autonomous robots or machines. The subject prepares students for applying methods practically, also in the Industry 4.0 direction.
Contents:
? Machine perception, observations, percepts, and their interpretation. Role of the context and semantics.
? Digital image. Image acquisition, physical viewpoint. Inverse task and unusability.
? Image processing. Detection of edge elements.
? Image segmentation.
? Statistical pattern recognition. Role of learning.
? Image object description and their classification using statistical pattern recognition methods.
? 3D vision, the geometry of one and more cameras. 3D reconstruction.
? Image acquisition hardware, depth maps, smart cameras.
? Computer vision applied in industry. Examples.
? Autonomous robots. World representation, its creation, and updates based on perception.
? Planning in autonomous robotics.
? Tactile feedback in robotics.
? Use of tactile and visual feedback in manipulation tasks.
? Cooperation of humans and robots in industry.
Recommended literature:
? M. Sonka, V. Hlavac, R. Boyle, Image processing, analysis, and machine vision, Fourth edition. Stamford, CT, USA: Cengage Learning, 2015.
? R. Szeliski, Computer vision: algorithms and applications. London?; New York: Springer, 2011.
? Fahimi, F.: Autonomous Robots: Modeling, Path Planning, and Control, Springer 2009

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