Self-Configuring and Evolving Fuzzy Image Thresholding
Abstract
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 SC-EFIS relies on a feature selection process that does not require the detection of a region of interest (ROI).
Cite
@article{arxiv.1509.04664,
title = {Self-Configuring and Evolving Fuzzy Image Thresholding},
author = {A. Othman and H. R. Tizhoosh and F. Khalvati},
journal= {arXiv preprint arXiv:1509.04664},
year = {2016}
}
Comments
To appear in proceedings of The 14th International Conference on Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA, 2015