Machine Vision Algorithms and Applications
2. Edition January 2018
516 Pages, Softcover
Die zweite Auflage dieses erfolgreichen Lehrbuchs zum maschinellen Sehen ist vollständig überarbeitet und erweitert, um die Entwicklungen der vergangenen Jahre auf den Gebieten der Bilderfassung, Algorithmen des maschinellen Sehens und dessen Anwendungen zu berücksichtigen.
The second edition of this successful machine vision textbook is completely updated, revised and expanded by 35% to reflect the developments of recent years in the fields of image acquisition, machine vision algorithms and applications. The new content includes, but is not limited to, a discussion of new camera and image acquisition interfaces, 3D sensors and technologies, 3D reconstruction, 3D object recognition and state-of-the-art classification algorithms. The authors retain their balanced approach with sufficient coverage of the theory and a strong focus on applications. All examples are based on the latest version of the machine vision software HALCON 13.
MACHINE VISION ALGORITHMS
Fundamental Data Structures
Segmentation and Fitting of Geometric Primitives
Optical Character Recognition
MACHINE VISION APPLICATIONS
Reading of Serial Numbers
Inspection of Saw Blades
Inspection of Ball Grid Arrays
Measuring of Spark Plugs
Molding Flash Detection
Inspection of Punched Sheets
3D Plane Reconstruction with Stereo
Pose Verification and Resistors
Classification of Non-Woven Fabrics
Markus Ulrich studied Geodesy and Remote Sensing at the Technical University of Munich (TUM) and received his PhD degree from TUM in 2003. In 2003, he joined MVTec?s Research and Development department as a software engineer and became head of the research team in 2008. He has authored and co-authored scientific publications in the fields of photogrammetry and machine vision. Markus Ulrich is also a guest lecturer at TUM, where he teaches close-range photogrammetry. In 2017, he was appointed a Privatdozent (lecturer) at the Karlsruhe Institute of Technology (KIT) for the field of machine vision.
Christian Wiedemann studied Geodesy and Remote Sensing at the Technical University of Munich (TUM) and received his PhD degree from TUM in 2001. He has authored and co-authored more than 40 scientific publications in the fields of photogrammetry, remote sensing, and machine vision. In 2003, he joined MVTec's Research and Development department as a software engineer. Since 2008, he has held different leading positions at MVTec.