Machine Vision Algorithms and Applications

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John Wiley & Sons, 12.03.2018 - 516 Seiten
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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.
 

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Inhalt

PAL phase alternating line 56
1
FFT fast Fourier transform 124
2
BCS base coordinate system 324 325 336 337
9
Lenses
12
PinholeCameras
18
DepthofField
28
compact disk 134 135 377 378380383
49
5
52
DCS distributed control system 2
123
SAD sum of absolute gray value differences 250254 263267 271 275
250
SCARA Selective Compliant Arm for Robot Assembly 335 336
275
IO inputoutput 2 3 63 64 71 74
319
SSD sum of squared gray value differences 250254 263264 267 271 275
333
SVM support vector machine 359 361364
359
xix
371
References
461

PLL phaselocked loop 58
58
CLProtocol Camera Link Protocol 62 73 77
62
GenApi Generic application programming interface for configuring cameras
63
WWW World Wide Web
70
GPU graphics processing unit 369
71
CMOS complementary metaloxide semiconductor 11 41 4652 54
91
Machine Vision Algorithms
97
6
98
RANSAC random sample consensus 208 209 211
99

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Über den Autor (2018)

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.

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