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A Nonlinear Derivative Scheme Applied to Edge Detection
February 2010 (vol. 32 no. 2)
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.

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Index Terms:
Edge detection, regularization filter, edge localization, edge model, neighbor edge, discrete approach, nonlinear derivative, noises, performance measure.
Citation:
Olivier Laligant, Frédéric Truchetet, "A Nonlinear Derivative Scheme Applied to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 242-257, Feb. 2010, doi:10.1109/TPAMI.2008.282
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