Enhanced descriptions from Syndetics:

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.

Reviews provided by Syndetics

CHOICE Review

Machine vision in this book corresponds mainly to what cognitive scientists call early vision (initial processes applied to a 2-D image), but not more difficult cognitive tasks at the frontier of contemporary artificial intelligence, such as facial recognition. The first chapter surveys current technology for image acquisition, and the final chapter surveys industrial applications. The middle chapter on algorithms, the heart of the book, needs a better editorial design and expository strategy. Ready-made solutions come jumbled with general engineering advice. Steger and colleagues (all, MVTec Software GmbH, Munich) fail to set off the development of mathematical tools from modeling (reducing engineering problems to mathematical ones) and from efficient implementation. Terms appear before their definitions, and no typographical or editorial flag marks definitions. A lack of expert editorial oversight shows. For example, readers learning about optical character recognition encounter the term "compactness'' and must guess after searching the index that this coincides with "convexity,'' defined on the fly more than 100 pages earlier; the usage of "ergodic'' in connection with noise reduction has only a soft connection to the standard mathematical definition. It seems ironic that experts on information extraction have made it somewhat difficult for readers to extract the valuable information here. Summing Up: Recommended. Upper-division undergraduate through professional collections. D. V. Feldman University of New Hampshire

Author notes provided by Syndetics

Carsten Steger studied computer science at the Technical University of Munich (TUM) and received his PhD degree from TUM in 1998. In 1996, he co-founded the company MVTec, where he heads the Research department. He has authored and co-authored more than 80 scientific publications in the field of computer and machine vision. In 2011, he was appointed a TUM honorary professor for the field of computer vision.

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.