International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 4, No. 1, 8 Feb. 2012
Cover page and Table of Contents: PDF (size: 297KB)
Author(s)
Index Terms
High-Precision Measuring, Object Recognition, Practical Applications of Computer Vision
Abstract
In this paper, we develop and study a new algorithm to recognize and precisely measure keys for the ultimate purpose of physically duplicating them. The main challenge comes from the fact that the proposed algorithm must use a single picture of the key obtained from a regular desktop scanner without any special preparation. It does not use the special lasers, lighting systems, or camera setups commonly used for the purpose of key measuring, nor does it require that the key be placed in a precise position and orientation. Instead, we propose an algorithm that uses a wide range of image processing methods to discover all the information needed to identify the correct key blank and to find precise measures of the notches of the key shank from the single scanned image alone. Our results show that our algorithm can correctly differentiate between different key models and can measure the dents of the key with a precision of a few tenths of a millimeter.
Cite This Paper
Naeem Nematollahi,Richard Khoury,"A Case Study in Key Measuring Software", IJIGSP, vol.4, no.1, pp.1-11, 2012. DOI: 10.5815/ijigsp.2012.01.01
Reference
[1]J. Y. Kaminski, D. Knaan, A. Shavit. Single image face orientation and gaze detection. Machine Vision and Applications, Springer Berlin / Heidelberg, 21(1):85-98, 2009.
[2]R. Almblad, J. Blin, P. Jurczak. Method and apparatus for automatically making keys. U.S. Patent 5 807 042, 1998.
[3]P. R. Wills, R. F. Kromann, N. N. Axelrod, W. A. Schroeder, J. A. Berilla, B. Burba. Method and Apparatus for Using Light to Identify a Key. U.S. Patent 6 064 747, 2000.
[4]V. Yanovsky. Shadow image acquisition device. U.S. Patent 6 175 638, 2001.
[5]J. S. Titus, J. E. Bolkom. Key Measurement Apparatus and Method, U.S. Patent 6 406 227, 2002.
[6]J. Campbell, G. Heredia, M. A. Mueller. Key Identification System, U.S. Patent 6 836 553, 2004.
[7]J. S. Titus, W. Laughlin, J. E. Bolkom. Key Duplication Apparatus and Method. U.S. Patent 6 152 662, 2000.
[8]V. Yanovsky, A. Sirota. Key Imaging System and Method. U.S. Patent 6 449 381, 2002.
[9]R. M. Prejean. Key Manufacturing Method. U.S. Patent 6 647 308, 2003.
[10]S. Pacenzia, E. Casangrande, E. Foscan. Method To Identify a Key Profile, Machine To Implement The Method and Apparatus for the Duplication of Keys Utilizing the Machine. U.S. Patent 6 895 100, 2005.
[11]L. Benedetti, M. Corsini, P. Cignoni, M. Callieri, R. Scopigno. Color to gray conversions in the context of stereo matching algorithms. Machine Vision and Applications, Springer Berlin / Heidelberg, in press.
[12]E. Cuevas, D. Zaldivar, M. Pérez-Cisneros. Seeking multi-thresholds for image segmentation with learning automata. Machine Vision and Applications, Springer Berlin / Heidelberg, 22(5):805-818, 2011.
[13]R. C. Gonzalez, R. E. Woods. Digital Image Processing. Prentice Hall, Inc., 2nd ed., New Jersey, 2002.
[14]R. C. Gonzalez, R. E. Woods, S. L. Eddins. Digital Image Processing Using MATLAB. Tata McGraw Hill Education Private Limited, New Delhi, India, 2010.
[15]L. Fletcher. Binary Image Analysis. Available: http://users.cecs.anu.edu.au/~luke/cvcourse_files/online_notes/lectures_2D_2_binary_morph_6up.pdf, 2010.
[16]M. K. Hu, Visual pattern recognition by moment invariants. Information Theory, IRE Transactions, pp. 179-187, 1962.
[17]M. Heath, S. Sarkar, T. Sanocki and K. Bowyer. Comparison of Edge Detectors: A Methodology and Initial Study. Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition, pp.143–148, 1996.
[18]D. W. Harder, R. Khoury. Numerical Analysis for Engineering Available: https://ece.uwaterloo.ca/~dwharder/NumericalAnalysis/05Interpolation/, 2010.
[19]J. Flusser, T. Suk. Rotation Moment Invariants for Recognition of Symmetric Objects. Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, pp. 3784 – 3790, 2006.
[20]J. Flusser. On the Independence of Rotation Moment Invariants. Pattern Recognition, vol. 33, pp. 1405-1410, 2000
[21]M. Hamidi, A. Borji. Invariance analysis of modified C2 features: case study—handwritten digit recognition. Machine Vision and Applications, Springer Berlin / Heidelberg, 21(6):969-979, 2010.