Self-Configuring and Evolving Fuzzy Image Thresholding
This work addresses limitations in fuzzy image segmentation for practitioners, but it is incremental as it builds directly on an existing method.
The authors tackled the problem of parameter adjustment in segmentation algorithms by proposing a self-configuring version of an existing evolving fuzzy system (EFIS) to overcome its dependency on object detection for feature calculation. The result is SC-EFIS, which uses training data to auto-configure parameters and eliminates the need for region-of-interest detection.
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).