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Hardware Centric Machine Vision for High Precision Center of Gravity Calculation

Authors: Xin Cheng, Benny Thörnberg, Abdul Waheed Malik, Najeem Lawal

Abstract:

We present a hardware oriented method for real-time measurements of object-s position in video. The targeted application area is light spots used as references for robotic navigation. Different algorithms for dynamic thresholding are explored in combination with component labeling and Center Of Gravity (COG) for highest possible precision versus Signal-to-Noise Ratio (SNR). This method was developed with a low hardware cost in focus having only one convolution operation required for preprocessing of data.

Keywords: Dynamic thresholding, segmentation, position measurement, sub-pixel precision, center of gravity.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1085289

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[1] Larsson U., Zell C., Hyypp├ñ K., Wernersson Å.: Navigating an Articulated Vehicle and Reversing with a Trailer. Proceedings 1994 IEEE International Conference on Robotics and Automation, vol. 3, pp. 2398--2404, San Diego, USA (1994).
[2] Wolf W., Ozer C., Lv T.: Smart cameras as embedded systems. Computer, vol. 35, no. 9 (2002).
[3] Dias F., Berry F., Serot J., Marmoiton F.: Hardware, design and implementation issues on a fpga-based smart camera. Proc. First ACM/IEEE international conference on distributed smart cameras. pp 20--26, Vienna, Austria (2007).
[4] Carsten Steger, Markus Ulrich and Christian Wiedemann,Machine vision algorithms and applications, Wiley-VCH 2008.
[5] Wnuk M.: Remarks on hardware implementation of image processing algorithms. Int. journal of applied mathematics and computer science. Vol. 18, No. 1, pp105--110 (2008).
[6] H. C. van Assen, M. Egmont-Petersen, and J. H. C. Reiber, "Accurate Object Localization in Gray Level Images Using the Center of Gravity Measure: Accuracy Versus Precision, IEEE Transaction on Image Processing," Vol. 11, No.12 December 2002.
[7] R.C. Gonzales and R.E. Woods, Addison Wesley,Digital Image Processing, 2008, third edition.
[8] A. Patwardhan, Subpixel position measurement using 1D,2D and 3D centroid algorithms with emphasis on applications in confocal microscopy, Journal of Microscopy, Vol. 186,Pt 3, June 1997, pp. 246- 257.
[9] Alexander Fish, Dmitry Akselrod and Orly Yadid-Pecht-Pecht, High Precision Image Centroid Computation via an Adaptive K-Winner- Take-all Circuit in Conjunction with a Dynamic Element Matching Algorithm for Star Tracking Applications, Analog Integrated Circuits and Signal Processing, 39, 251-266, 2004.
[10] G.A.W. West, & T.A. Clarke, 1990, "A survey and examination of subpixel measurement techniques.", ISPRS Int. Conf. on Close Range Photogrammetry and Machine Vision, SPIE Vol. 1395, pp 456 - 463, Sept. 3-7.
[11] Clarke, T.A. Cooper, M.A.R. & Fryer, J.G., 1993. An estimator for the random error in subpixel target location and its use in the bundle adjustment. Optical 3-D measurements techniques II, Pub. Wichmann, Karlsruhe:161-168.
[12] B.Thörnberg et al. "Bit-Width Constrained Memory Hierarchy Optimization for Real-Time Video Systems", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol26, No 4, pp 781-800, April 2007.
[13] B. Thörnberg and N. Lawal, "Real-time component labelling and feature extraction on FPGA", Proc. of International Symposium on Signals, Circuits and Systems, Iasi, Romania 2009,