A Nonlinear Derivative Scheme Applied to Edge Detection

Authors:
Olivier Laligant;Frederic Truchetet
Affiliations:
Université de Bourgogne, Le Creusot;Université de Bourgogne, Le Creusot
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2010

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Abstract

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.