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.

• 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.

• 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