A survey of subpixel edge detection methods for images of heat-emitting metal specimens

• Autorzy: Anna Fabijańska
• Języki publikacji: EN

Abstrakty

EN

In this paper the problem of accurate edge detection in images of heat-emitting specimens of metals is discussed. The images are provided by the computerized system for high temperature measurements of surface properties of metals and alloys. Subpixel edge detection is applied in the system considered in order to improve the accuracy of surface tension determination. A reconstructive method for subpixel edge detection is introduced. The method uses a Gaussian function in order to reconstruct the gradient function in the neighborhood of a coarse edge and to determine its subpixel position. Results of applying the proposed method in the measurement system considered are presented and compared with those obtained using different methods for subpixel edge detection.

Słowa kluczowe

EN

subpixel accuracy; edge detection; surface property; approximation; Gaussian function

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2012
• Tom: 22
• Numer: 3
• Strony: 695-710

Daty

wydano

2012

otrzymano

2011-07-07

poprawiono

2011-12-16

Twórcy

autor

Anna Fabijańska

• Institute of Applied Computer Science, Łódź University of Technology, Stefanowskiego 18/22, 90-924 Łódź, Poland

Bibliografia

• Adamson, A. and Gast, A. (1997). Physical Chemistry of Surface, Wiley, Hoboken, NJ.
• Bachevsky, R. S., Naidich, Y. V., Grygorenko, M. F. and Dostojny, V. A. (1994). Evaluation of errors in automatic image analysis determination of sessile drop shapes, Proceedings of the International Conference on High Temperature Capillarity, Sanremo, Italy, pp. 254-258.
• Bailey, D.G. (2005). Sub-pixel profiling, Proceedings of the 5th International Conference on Information Communications and Signal Processing, Bangkok, Thailand, pp. 1311-1315.
• Batchelor, B. and Whelan, P. (2002). Intelligent Vision Systems for Industry, http://elm.eeng.dcu.ie/˜whelanp/ivsi/.
• Bie, H.X. and Liu C.Y. (2009). Edge-directed sub-pixel and still image super-resolution, Proceedings of the 2nd International Congress on Image and Signal Processing, Tianjin, China, pp. 1-4
• Bin, T.J., Lei, A., Jiwen, C., Wenjing, K. and Dandan, L. (2008). Subpixel edge location based on orthogonal Fourier-Mellin moments, Image and Vision Computing 26(4): 563-569.
• Breder R., Estrela V.-V. and de Assis J.T. (2009). Sub-pixel accuracy edge fitting by means of b-spline, Proceedings of the IEEE International Workshop on Multimedia Signal Processing, Rio De Janeiro, Brazil, pp. 1-5.
• Dennis, J.E. andSchnabel, R.B. (1983). Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice Hall, Upper Saddle River, NJ.
• Extrand, C.W. and Moon, S.I. (2010). When sessile drops are no longer small: Transitions from spherical to fully flattened, Langmuir 26(14): 11815-11822.
• Fabijańska, A. and Sankowski, D. (2009). Computer vision system for high temperature measurements of surface properties, Machine Vision and Applications 20(6): 411-421.
• Fabijańska, A. and Sankowski, D. (2010). Edge detection with sub-pixel accuracy in images of molten metals, IEEE International Conference on Imaging Systems and Techniques, Thessaloniki, Greece, pp. 186-191.
• Gocławski, J., Sekulska-Nalewajko, J., Gajewska, E. and Wielanek, M. (2009). An automatic segmentation method for scanned images of wheat root systems with dark discolourations, International Journal of Applied Mathematics and Computer Science 19(4): 679-689, DOI: 10.2478/v10006-009-0055-x.
• Gonzalez, R.C. and Woods, R.E. (2007). Digital Image Processing, 3rd Edn., Prentice Hall, Upper Saddle River, NJ.
• Ghosal, S. and Mehrotra, R. (1993). Orthogonal moment operators for subpixel edge detection, Pattern Recognition Letters 26(2): 295-305.
• Hansen, F.K. (1993). Surface tension by image analysis: Fast and automatic measurements of pendant and sessile drops and bubbles, Journal of Colloid and Interface Science 160(1): 209-217.
• Huh, C. and Reed, R.L. (1983). A method for estimating interfacial tensions and contact angles from sessile and pendant drop shapes, Journal of Colloid and Interface Science 91(2): 472-484.
• Ji X., Wang, K. and Wei, Z. (2009). Structured light encoding research based on sub-pixel edge detection, Proceedings of the International Conference on Information Engineering and Computer Science, Wuhan, China, pp. 1-4.
• Jin, J.S. (1990). An adaptive algorithm for edge detection with subpixel accuracy in noisy images, Proceedings of the IAPR Workshop on Machine Vision Applications, Tokyo, Japan, pp. 249-252.
• Kisworo, M., Venkatesh, S. and West, G. (1991). 2-D edge feature extraction to subpixel accuracy using the generalized energy approach, Proceedings of the IEEE Region 10 International Conference on EC3-Energy, Computer, Communication and Control Systems, New Delhi, India, pp. 344-348.
• Koszmider, T., Strzecha, K., Fabijańska, A. and Bakala, M. (2011). Algorithm for accurate determination of contact angles in vision system for high temperature measurements of metals and alloys surface properties, in R. Burduk, M. Kurzyński, M. Woźniak and A. Żołnierek (Eds.), Computer Recognition Systems 4, Advances in Intelligent and Soft Computing, Vol. 95, Springer, Berlin/Heidelberg, pp. 441-448, DOI: 10.1007/978-3-642-20320-6 46.
• Liu, C., Xia Z., Niyokindi, S., Pei, W., Song, J. and Wang, L. (2004). Edge location to sub-pixel value in color microscopic images, Proceedings of the International Conference on Intelligent Mechatronics and Automation, Chengdu, Sichuan, China, pp. 548-551.
• Lyvers, E.P., Mitchell, O.R., Akey, M.L. and Reeves, A.P. (1989). Subpixel mesurements using a moment-basededge operator, IEEE Transactions on Pattern Analysis and Machine Intelligence 11(12): 1293-1309.
• Machuca, R. and Gilbert, A.L. (1981). Finding edges in noisy scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence 3(1): 103-111.
• MacVicar-Whelan, P.J. and Binford, T.O. (1981). Intensity discontinuity location to subpixel precision, Proceedings of the International Joint Conference on Artificial Intelligence, Vancouver, BC, Canada, pp. 26-31.
• MacVicar-Whelan, P.J. and Binford, T.O. (1991). Line finding with subpixel precision, Proceedings of the DARPA Image Understanding Workshop, Washington, DC, USA, pp. 26-31.
• Mills, K.C. and Su, Y.C (2006). Review of surface tension data for metallic elements and alloys, Part 1: Pure metals, International Materials Reviews 51(6): 329-351.
• Nevtia, R. and Babu, K. (1978). Linear feature extraction, Proceedings of the DARPA Image Understanding Workshop, Pittsburgh, PA, USA, pp. 73-78.
• Obinata, G. and Dutta, A. (2007). Vision Systems: Applications, I-Tech Education and Publishing, Vienna.
• Oskoei, M.A. and Hu, H. (2010). A survey on edge detection methods, Technical Report CES-506, University of Essex, Essex.
• Ranky, P. (2003). Advanced machine vision systems and application examples, Sensor Review 23(3): 242-245.
• Ridler, T. and Calvard, S. (1978). Picture thresholding using an iterative selection method, IEEE Transations on Systems, Man, and Cybernetics 8(8): 630-632
• Rocket, P. (1999). The accuracy of sub-pixel localization in the Canny edge detector, Proceedings of the British Machine Vision Conference, Nottingham, UK, pp. 392-401.
• Sankowski, D., Strzecha, K. and Jezewski, S. (2000). Digital image analysis in measurement of surface tension and wettability angle, Proceedings of the International Conference on Modern Problems of Telecommunications, Computer Science and Engineers Training, Lviv/Slavsko, Ukraine, pp. 129-130.
• Scott, N. (2010). Remote Sensing Tutorial, http://rst.gsfc.nasa.gov/Sect13/Sect13_2.html.
• Senthilkumaran, N. and Rajesh, R. (2009). Edge detection techniques for image segmentation: A survey of soft computing approaches, International Journal of Recent Trends in Engineering 1(2): 250-254.
• Sheng, Y. and Shen, L. (1994). Orthogonal Fourier-Mellin moments for invariant pattern recognition, Journal of the Optical Society of America 11(6): 1748-1757.
• Sidiropoulos, N.D., Baras, J.S. and Berenstein, C.A. (1992). Discrete random sets: An inverse problem, plus tools for the statistical inference of the discrete Boolean model, Proceedings of SPIE 1769(1):32-43.
• Stanke, G., Zedler, L., Zorn, A., Weckend, F. and Weide, H.G. (1998). Sub-pixel accuracy by optical measurement of large automobile components, Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society, Aachen, Germany, pp. 2431-2433.
• Steger, C., Ulrich, M. and Wiedemann, C. (2008). Machine Vision Algorithms and Applications, Wiley-VCH, Berlin.
• Strzecha, K., Bakala, M., Fabijańska, A. and Koszmider. T (2010). The evolution of Thermo-Wet: The computerized system for measurements of surface properties, Automatyka 3(14): 525-535, (in Polish).
• Tabatabai, A.J. and Mitchell, O.R. (1984). Edge location to subpixel values in digital imagery, IEEE Transactions on Pattern Analysis and Machine Intelligence 6(2): 188-20l.
• Xu, G.S. (2009a). Sub-pixel edge detection based on curve fitting, Proceedings of the 2nd International Conference on Information and Computing Science, Manchester, UK, pp. 373-375.
• Xu, G.S. (2009b). Linear array CCD image sub-pixel edge detection based on wavelet transform, Proceedings of the 2nd International Conference on Information and Computing Science, Manchester, UK, pp. 204-206.
• Yao, Y. and Ju, H. (2009). A sub-pixel edge detection method based on canny operator, Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, China, pp. 97-100.

Identyfikatory

DOI

10.2478/v10006-012-0052-3