Prof. Kurt S. Niel Profile

Prof. Kurt S. Niel

Head of Dept. Metrology & Control Engineering at FH Oberoesterreich

SPIE Involvement:

Editor | Author | Instructor

Websites:

Company Website | Personal Website

Proceedings Article | 7 March 2014 Paper

Trends and developments in industrial machine vision: 2013

Kurt Niel, Christoph Heinzl

Proceedings Volume 9024, 902405 (2014) https://doi.org/10.1117/12.2042501

KEYWORDS: Machine vision, Visualization, Computing systems, Metrology, Cameras, Sensors, Data processing, Image processing, Visual analytics, Robotics

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When following current advancements and implementations in the field of machine vision there seems to be no borders for future developments: Calculating power constantly increases, and new ideas are spreading and previously challenging approaches are introduced in to mass market. Within the past decades these advances have had dramatic impacts on our lives. Consumer electronics, e.g. computers or telephones, which once occupied large volumes, now fit in the palm of a hand. To note just a few examples e.g. face recognition was adopted by the consumer market, 3D capturing became cheap, due to the huge community SW-coding got easier using sophisticated development platforms. However, still there is a remaining gap between consumer and industrial applications. While the first ones have to be entertaining, the second have to be reliable. Recent studies (e.g. VDMA [1], Germany) show a moderately increasing market for machine vision in industry. Asking industry regarding their needs the main challenges for industrial machine vision are simple usage and reliability for the process, quick support, full automation, self/easy adjustment at changing process parameters, "forget it in the line". Furthermore a big challenge is to support quality control: Nowadays the operator has to accurately define the tested features for checking the probes. There is an upcoming development also to let automated machine vision applications find out essential parameters in a more abstract level (top down). In this work we focus on three current and future topics for industrial machine vision: Metrology supporting automation, quality control (inline/atline/offline) as well as visualization and analysis of datasets with steadily growing sizes. Finally the general trend of the pixel orientated towards object orientated evaluation is addressed. We do not directly address the field of robotics taking advances from machine vision. This is actually a fast changing area which is worth an own contribution.

SPIE Journal Paper | 1 July 2010 Open Access

Special Section Guest Editorial: Quality Control for Artificial Vision

Edmund Lam, Shaun Gleason, Kurt Niel

JEI, Vol. 19, Issue 3, 031201, (July 2010) https://doi.org/10.1117/12.10.1117/1.3497000

KEYWORDS: Machine vision, Image segmentation, Imaging systems, Image quality, Control systems, Inspection, Image processing, Biomedical optics, Electronics engineering, 3D image processing

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Proceedings Article | 26 February 2008 Paper

Optical measurement system for characterizing plastic surfaces

R. Gahleitner, Kurt Niel, S. Frank

Proceedings Volume 6813, 68130T (2008) https://doi.org/10.1117/12.766311

KEYWORDS: Cameras, Image filtering, Optical testing, Visualization, Light emitting diodes, Reflection, Image processing, Signal processing, Gaussian filters, Light sources

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Injection molded plastic parts are often influenced with the surface defect tiger stripes, which dramatically reduce the visual quality. Tiger stripes are known as alternating bands of bright and dull regions normally to the molded flow direction. This defect highly depends on the injection time and on the formation of the plastic compound. In the last years, the intensity of the tiger stripes defect was controlled visually. For quantifying the tiger strip defect a new, efficient, repeatable, reliable and nondestructive optical measurement system is proposed. To evaluate the dependency of the injection time, a number of five DIN-A5 plastic specimens are molded. Each of the five plates consists of the same material but they have different injection times. For the measurement, one specimen is put into the specimen holder, which is placed on the drawer of a closed cabinet. In this inside black painted cabinet a LED light source and a CCD Camera are mounted. The beams of the LED light are diffuse reflected on the surface of the specimen. To catch only parallel beams by the lens of the camera a large distance between specimen and camera is realized by two justified mirrors in the cabinet. The bright and dull regions of the tiger stripe defect have different diffuse reflection parameters. Thus in a picture of defined brightness the visibility of this defect is very good. To enhance the repeatability the failure of the camera noise and of the light oscillation is reduced by mends of averaging multiple images. Next, the surface structure is filtered out of the image and a representing number of horizontal grey-value lines are extracted. The so called tiger line signal is the difference between the grey line and a calculated polynomial function (degree of 6) and shows the surface defect of each line oscillating on the zero x-axis. For each tiger line signal the mean squared error is evaluated. To calculate a quantitative value of the whole surface, all line errors are averaged to the so called MSE-value. Measurements and comparisons show, that this MSE-value represents surface defects and especially the intensity of tiger stripes very good. The repeating error is lower than 1 %. Experiments for showing unknown effects of normal and of accelerated aging and weathering of plastic surfaces were done successfully.

Proceedings Article | 26 February 2008 Paper

Geometric in-line inspection of profiled strips and welding seams

Johann Reisinger, Kurt Niel, Mark Tratnig

Proceedings Volume 6813, 68130Y (2008) https://doi.org/10.1117/12.779413

KEYWORDS: Inspection, Head, Sensors, Calibration, Optical testing, Profiling, Cameras, Human-machine interfaces, Image registration, Visualization

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Proceedings Article | 17 February 2007 Paper

Appliance of stereoscopy for bath level detection of steel casting pans

Christian Rittenschober, Kurt Niel, Roman Rössler

Proceedings Volume 6503, 65030C (2007) https://doi.org/10.1117/12.713107

KEYWORDS: Cameras, Imaging systems, Distortion, Stereoscopic cameras, Algorithm development, Stereoscopy, Environmental sensing, Mechanical engineering, Image processing, Liquids

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Proceedings Article | 9 February 2006 Paper

Flushing analysis by machine vision and fuzzy logic at molten steel for the automation process

Christian Pfob, Kurt Niel, Roman Roessler

Proceedings Volume 6070, 607008 (2006) https://doi.org/10.1117/12.648737

KEYWORDS: Fuzzy logic, Image processing, Cameras, Machine vision, Image filtering, RGB color model, Binary data, Homogenization, Failure analysis, Control systems

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Proceedings Article | 24 February 2005 Paper

Camber measurement at hot strip mill at Voestalpine by using image processing method

Helmut Hlobil, Kurt Niel, Markus Prinz, Gerhard Trinkl, Wolfgang Seyruck, Johann Reisinger

Proceedings Volume 5679, (2005) https://doi.org/10.1117/12.595073

KEYWORDS: Cameras, CCD cameras, Imaging systems, Image processing, Reliability, Optical testing, Distortion, Computer networks, Head, Mechatronics

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Proceedings Article | 10 September 2004 Paper

Processing of low-intensity CCD images for temperature mapping in industrial processes

Gerald Zauner, Daniel Heim, Kurt Niel, Guenther Hendorfer, Herbert Stoeri

Proceedings Volume 5457, (2004) https://doi.org/10.1117/12.545372

KEYWORDS: Cameras, Interference (communication), Image processing, Temperature metrology, Optical filters, CCD cameras, Image filtering, Charge-coupled devices, Black bodies, Thermography

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Proceedings Article | 3 May 2004 Paper

CCD cameras as thermal imaging Devices in heat treatment processes

Gerald Zauner, Daniel Heim, Kurt Niel, Gunther Hendorfer, Herbert Stoeri

Proceedings Volume 5303, (2004) https://doi.org/10.1117/12.526339

KEYWORDS: Temperature metrology, Thermography, CCD cameras, Near infrared, Cameras, Imaging systems, Optical filters, Black bodies, Interference (communication), Charge-coupled devices

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Proceedings Article | 22 May 2003 Paper

Temperature mapping in heat treatment processes with a standard color-video camera by means of image processing

Gerald Zauner, Daniel Heim, Gunther Hendorfer, Kurt Niel

Proceedings Volume 5011, (2003) https://doi.org/10.1117/12.477505

KEYWORDS: Image processing, Cameras, Temperature metrology, Visualization, Imaging systems, Image stacking, Image enhancement, Image visualization, CCD cameras, Image filtering

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Showing 5 of 10 publications

SPIE Conference Volume | 3 April 2015

Image Processing: Machine Vision Applications VIII

SPIE Conference Volume | 10 March 2014

Image Processing: Machine Vision Applications VII

SPIE Conference Volume | 28 January 2010

Image Processing: Machine Vision Applications III

SPIE Conference Volume | 2 February 2009

Image Processing: Machine Vision Applications II

SPIE Conference Volume | 26 February 2008

Image Processing: Machine Vision Applications

Showing 5 of 7 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

Intelligent Robots and Computer Vision XXX: Algorithms and Techniques

4 February 2013 | Burlingame, California, United States

Image Processing: Machine Vision Applications V

25 January 2012 | Burlingame, California, United States

Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques

23 January 2012 | Burlingame, California, United States

10th International Conference on Quality Control by Artificial Vision

28 June 2011 | Saint-Etienne, France

Image Processing: Machine Vision Applications IV

25 January 2011 | San Francisco Airport, California, United States

Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques

24 January 2011 | San Francisco Airport, California, United States

Image Processing: Machine Vision Applications III

20 January 2010 | San Jose, California, United States

Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques

18 January 2010 | San Jose, California, United States

Image Processing: Machine Vision Applications II

22 January 2009 | San Jose, California, United States

Intelligent Robots and Computer Vision XXVI: Algorithms and Techniques

19 January 2009 | San Jose, California, United States

Image Processing: Machine Vision Applications

29 January 2008 | San Jose, California, United States

Machine Vision Applications in Industrial Inspection XV

29 January 2007 | San Jose, CA, United States

Machine Vision Applications in Industrial Inspection XIV

16 January 2006 | San Jose, California, United States

Machine Vision Applications in Industrial Inspection XIII

17 January 2005 | San Jose, California, United States

Machine Vision Applications in Industrial Inspection XII

21 January 2004 | San Jose, California, United States

Showing 5 of 20 Conference Committees

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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