The Community for Technology Leaders
RSS Icon
Issue No.02 - February (2010 vol.32)
pp: 242-257
Olivier Laligant , Université de Bourgogne, Le Creusot
Frédéric Truchetet , Université de Bourgogne, Le Creusot
This paper presents a nonlinear derivative approach to addressing the problem of discrete edge detection. This edge detection scheme is based on the nonlinear combination of two polarized derivatives. Its main property is a favorable signal-to-noise ratio ({SNR}) at a very low computation cost and without any regularization. A 2D extension of the method is presented and the benefits of the 2D localization are discussed. The performance of the localization and {SNR} are compared to that obtained using classical edge detection schemes. Tests of the regularized versions and a theoretical estimation of the {SNR} improvement complete this work.
Edge detection, regularization filter, edge localization, edge model, neighbor edge, discrete approach, nonlinear derivative, noises, performance measure.
Olivier Laligant, Frédéric Truchetet, "A Nonlinear Derivative Scheme Applied to Edge Detection", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 2, pp. 242-257, February 2010, doi:10.1109/TPAMI.2008.282
[1] J.W. Modestino and R.W. Fries, “Edge Detection in Noisy Images Using Recursive Digital Filtering,” Computer Graphics and Image Processing, vol. 6, pp. 409-433, 1977.
[2] J.-O. Eklundh, T. Elfving, and S. Nyberg, “Edge Detection Using the Marr-Hildreth Operator with Different Sizes,” Proc. IEEE Int'l Conf. Pattern Recognition, vol. 6, pp. 1109-1112, 1982.
[3] P. Bolon, A. Raji, P. Lambert, and M. Mouhoub, “Recursive Median Filters—Application to Noise Reduction and Edge Detection” Proc. Fifth European Signal Processing Conf., pp. 813-816, Sept. 1990.
[4] V. Torre and T.A. Poggio, “On Edge Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 2, pp. 147-163, Mar. 1986.
[5] J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp.679-698, Nov. 1986.
[6] D. Ziou, “Line Detection Using Optimal IRR Filter,” Pattern Recognition, vol. 24, no. 6, pp. 465-478, 1991.
[7] R. Deriche, “Using Canny's Criteria to Derive a Recursively Implemented Optimal Edge Detector,” Int'l J. Computer Vision, vol. 1, no. 2, pp. 167-187, May 1987.
[8] E. Bourennane, P. Gouton, M. Paindavoine, and F. Truchetet, “Generalization of Canny-Deriche Filter for Detection of Noisy Exponential Edge,” Signal Processing, vol. 12, no. 10, pp. 1317-1328, Oct. 2002.
[9] M. Jacob and M. Unser, “Design of Steerable Filters for Feature Detection Using Canny-Like Criteria,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 8, pp.1007-1019, Aug. 2004.
[10] K.L. Boyer and S. Sarkar, “On the Localization Performance Measure and Optimal Edge Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 1, pp. 106-110, Jan. 1994.
[11] S. Kumar, S.H. Ong, S. Ranganath, and F.T. Chew, “A Luminance- and Contrast-Invariant Edge-Similarity Measure,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2042-2048, Dec. 2006.
[12] D. Demigny and T. Kamlé, “A Discrete Expression of Canny's Criteria for Step Edge Detector Performances Evaluation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 11, pp.1199-1211, Nov. 1997.
[13] F. Truchetet, F. Nicolier, and O. Laligant, “Supixel Edge Detection for Dimensional Control by Artificial Vision,” J. Electronic Imaging, vol. 10, no. 1, pp. 234-239, Jan. 2001.
[14] D. Demigny, “On Optimal Linear Filtering for Edge Detection,” IEEE Trans. Image Processing, vol. 11, no. 7, pp. 728-737, July 2002.
[15] O. Laligant, F. Truchetet, and F. Mériaudeau, “Regularization Preserving Localization of Close Edges,” IEEE Signal Processing Letters, vol. 14, no. 3, pp. 185-188, Mar. 2007.
[16] I. Pitas and A. Venetsanopoulos, “Nonlinear Mean Filters in Image Processing,” IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 34, no. 3, pp. 573-584, June 1986.
[17] A. Benazza-Benyahia, J.-C. Pesquet, and H. Krim, “A Nonlinear Diffusion-Based 3-Band Filter Bank,” IEEE Signal Processing Letters, vol. 10, pp. 360-363, Dec. 2003.
[18] M.A. Schulze, “An Edge-Enhancing Nonlinear Filter for Reducing Multiplicative Noise,” Nonlinear Image Processing VIII, pp. 46-56, SPIE, 1997.
[19] H. Hwang and R. Haddad, “Multilevel Nonlinear Filters for Edge Detection and Noisesuppression,” IEEE Trans. Signal Processing, vol. 42, no. 2, pp. 249-258, Feb. 1994.
[20] I.E. Abdou and W.K. Pratt, “Quantitative Design and Evaluation of Enhancement/Thresholding Edge Detectors,” Proc. IEEE, vol. 67, no. 5, pp. 753-763, May 1979.
[21] S. Tabbone and D. Ziou, “Subpixel Positioning of Edges for First and Second Order Operators,” Proc. Int'l Conf. Pattern Recognition, pp. 655-658, 1992.
[22] K. Chen, “Adaptative Smoothing via Contextual and Local Discontinuities,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1552-1567, Oct. 2006.
[23] M. Petrou and J. Kitler, “Optimal Edge Detectors for Ramp Edges,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 5, pp. 483-491, May 1991.
[24] D. Martin, C. Fowlkes, and J. Malik, “Learning to Detect Natural Image Boundaries Using Local Brightness, Color and Texture Cues,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 530-549, May, 2004.
15 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool