Every segmentation algorithm has parameters that
need to be adjusted in order to achieve good res... more Every segmentation algorithm has parameters that need to be adjusted in order to achieve good results. Evolving fuzzy systems for adjustment of segmentation parameters have been proposed recently (Evolving fuzzy image segmentation – EFIS [1]). However, similar to any other algorithm, EFIS too suffers from a few limitations when used in practice. As a major drawback, EFIS depends on detection of the object of interest for feature calculation, a task that is highly application-dependent. In this paper, a new version of EFIS is proposed to overcome these limitations. The new EFIS, called self-configuring EFIS (SC-EFIS), uses available training data to auto-configure the parameters that are fixed in EFIS. As well, the proposed SCEFIS relies on a feature selection process that does not require the detection of a region of interest (ROI).
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Papers by A. Othman
need to be adjusted in order to achieve good results. Evolving
fuzzy systems for adjustment of segmentation parameters have
been proposed recently (Evolving fuzzy image segmentation –
EFIS [1]). However, similar to any other algorithm, EFIS too
suffers from a few limitations when used in practice. As a major
drawback, EFIS depends on detection of the object of interest for feature calculation, a task that is highly application-dependent.
In this paper, a new version of EFIS is proposed to overcome
these limitations. The new EFIS, called self-configuring EFIS
(SC-EFIS), uses available training data to auto-configure the
parameters that are fixed in EFIS. As well, the proposed SCEFIS
relies on a feature selection process that does not require
the detection of a region of interest (ROI).