Prototyping Industrial Vision Applications and Implementations on Multimedia Processors

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The article presents a development system for industrial vision applications based on multimedia processors. For simulation of the algorithm and prototyping the application we can use either standard Matlab / Simulink environment or specialized environments such as Open eVision. The system components are presented with examples of application prototyping and implementation on standard platform Digital Video Development Platform DVDP6437 from Spectrum Digital, equipped with Texas Instruments TMS320DM6437. In the presented system we did experiments regarding the Rapid Prototyping concept in industrial vision application, starting from Embedded Target Library components from Matlab / Simulink. The experiments revealed that multimedia processors are usable both in video and still image processing and are possible to be considered an option in standard industrial vision applications: positioning, visual inspection. The paper contains also a brief study of the necessary components of a proposed embedded architecture, intended to be realized for evaluation of the technology in industrial environments.

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321-326

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November 2015

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] G. Bradski, A. Kaehler, Learning OpenCV, first ed., O'Reilly Media, Sebastopol, (2008).

Google Scholar

[2] Information on http: /www. raspberrypi. org/ [last accessed, July 25, 2015].

Google Scholar

[3] Information on http: /beagleboard. org/beagleboard-xm [last accessed, July 25, 2015].

Google Scholar

[4] H. Golnabi, A. Asadpour, Design and application of industrial machine vision systems, Robotics and Computer-Integrated Manufacturing, 23(6), 2007, p.630–637.

DOI: 10.1016/j.rcim.2007.02.005

Google Scholar

[5] R. Labudzki, S. Legutko, P. Raos, The essence and applications of machine vision, Tehnički Vjesnik - Technical Gazette, 21(4), 2014, pp.903-909.

Google Scholar

[6] E. N Malamas, E. G. M Petrakis, M. Zervakis, L. Petit, J-D Legat, A survey on industrial vision systems, applications and tools in: M. Frahm, M. Pantic (eds. ), Image and Vision Computing, Springer, 2003, pp.171-188.

DOI: 10.1016/s0262-8856(02)00152-x

Google Scholar

[7] Xiao Jun He, Zhen Di Yi, Jing Liu, Yu Zheng Wang, Defect Detecting Technology Based on Machine Vision of Industrial Parts, in: Applied Mechanics and Materials (Volumes 641-642), pp.1275-1279.

DOI: 10.4028/www.scientific.net/amm.641-642.1275

Google Scholar

[8] Z. Jianjun, Z. Jianhong, Research on embedded digital image recognition system based on ARM-DSP, in: Proceedings of 2nd IEEE International Conference on Computer Science and Information Technology, 2009, pp.524-527.

DOI: 10.1109/iccsit.2009.5234893

Google Scholar

[9] Spectrum Digital, TMS320DM6437 Evaluation Module Technical Reference, (2006).

Google Scholar

[10] B. G. Batchelor. (Ed. ), Machine Vision Handbook, Springer, (2012).

Google Scholar

[11] Information on http: /www. euresys. com [last accessed, July, 25, 2015].

Google Scholar

[12] R. Arsinte, Aspects of Algorithm Development for Systems with Online Detection and Recognition of TV Commercials, Acta Technica Napocensis - Electronics and telecommunications, 50 (4), 2009, pp.38-42.

Google Scholar

[13] Chris Solomon, Toby Breckon, Fundamentals of Digital Image Processing - A Practical Approach with Examples in Matlab, Wiley-Blackwell, (2011).

DOI: 10.1002/9780470689776

Google Scholar

[14] S. Qureshi, Embedded Image Processing on the TMS320C6000™ DSP, Springer, (2005).

Google Scholar