Abstract
This paper describes two new pattern detection image operators, \(\Re_{1}^{riu2}\) and \(\Re_{2}\), called, in a generic way, LBP-based relational operators (LBP-RO). The former is rotational invariant and allows searching for a particular pattern disposes in any direction, the later is a binary operator designed to find image patterns that can be modeled by a pattern function. Both of them are invariants against any monotonic transformation of the image gray scale. We have applied these operators in a case study dedicated to segment the ONH in eye fundus color photographic images. The new segmentation method, called GA+LBP-RO, was compared to a competitive ONH segmentation method in the literature and the results obtained by our method proved to be equal to or better.
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Molina-Casado, J.M., Carmona, E.J. (2013). Pattern Detection in Images Using LBP-Based Relational Operators. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Computation in Engineering and Medical Applications. IWINAC 2013. Lecture Notes in Computer Science, vol 7931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38622-0_2
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DOI: https://doi.org/10.1007/978-3-642-38622-0_2
Publisher Name: Springer, Berlin, Heidelberg
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