Prof. Kurt S. Niel
Head of Dept. Metrology & Control Engineering at Upper Austria Univ of Applied Sciences
SPIE Involvement:
Conference Chair | Conference Program Committee | Editor | Author | Instructor
Publications (17)

SPIE Conference Volume | April 3, 2015

SPIE Conference Volume | March 10, 2014

PROCEEDINGS ARTICLE | March 7, 2014
Proc. SPIE. 9024, Image Processing: Machine Vision Applications VII
KEYWORDS: Metrology, Visual analytics, Visualization, Cameras, Sensors, Image processing, Robotics, Computing systems, Data processing, Machine vision

SPIE Journal Paper | July 1, 2010
JEI Vol. 19 Issue 3
KEYWORDS: Machine vision, Image segmentation, Imaging systems, Image quality, Control systems, Inspection, Image processing, Biomedical optics, Electronics engineering, 3D image processing

SPIE Conference Volume | January 28, 2010

SPIE Conference Volume | February 2, 2009

Showing 5 of 17 publications
Conference Committee Involvement (20)
Image Processing: Machine Vision Applications VIII
10 February 2015 | San Francisco, California, United States
Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques
9 February 2015 | San Francisco, California, United States
Intelligent Robots and Computer Vision XXXI: Algorithms and Techniques
5 February 2014 | San Francisco, California, United States
Image Processing: Machine Vision Applications VII
3 February 2014 | San Francisco, California, United States
Image Processing: Machine Vision Applications VI
5 February 2013 | Burlingame, California, United States
Showing 5 of 20 published special sections
Course Instructor
SC767: Practical Implementations of Machine Vision Systems within Technical Processes
This course intends to support engineers who need to develop and design machine vision applications in the industry, concentrating on information outside the central technology of image processing. The participants will get an idea about the necessary considerations, outside the main algorithms and the fundamental architecture of such a system, necessary to implement machine vision systems within an automated industrial process. It will be shown that many other factors during implementation can strengthen or weaken the probability of the success of a durable machine vision system.
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