Edge detection by associative mapping

https://doi.org/10.1016/0031-3203(89)90019-8Get rights and content

Abstract

A new set of template matching edge operators is developed using an associative mapping between ideal step edges defined on a 3 × 3 neighborhood and the orthonormal basis of the neighborhood regarded as a nine-dimensional vector space. Detection of an edge is based only on the confidence in the goodness of fit to a template and the performance does not deteriorate for low amplitudes. The method can be adapted to the specific needs of the user; the set of masks can be reduced trading orientation resolution for amount of computation; and edges can be thresholded adaptively to the local background level. When applied in a dual way, the edge detection procedure provides an estimate of the standard deviation of the noise present in the image. Optimality for step edge detection of one of the 3 × 3 Laplacian operators is shown. The edge images obtained from the new edge operators are suitable inputs into relaxation algorithms based on local consistency.

References (11)

There are more references available in the full text version of this article.

Cited by (14)

  • Two-dimensional multi-pixel anisotropic Gaussian filter for edge-line segment (ELS) detection

    2014, Image and Vision Computing
    Citation Excerpt :

    Due to the above two drawbacks, this paper will emphasize on the local methods. The computationally simple local operators mainly include the differentiation-based methods [12], the Gaussian-based methods [13–27], multi-scale methods based on wavelets [28–32], computational neural networks [33–36], fuzzy reasoning systems [37–41], Gabor filters [42–47], and two-dimensional matched filter (TDMF) [48,49]. It is especially worth mentioning that Canny developed the isotropic operator [22] with an almost optimal vertical filter that is very well approximated by the first order derivative of a Gaussian filter.

View all citing articles on Scopus
View full text