@Article{CSIAM-AM-2-4,
author = {Wei, Wang,  and Li, Caifei,  and Ng, Michael, K.},
title = {A Saturation-Component Based Fuzzy Mumford-Shah Model for Color Image Segmentation},
journal = {CSIAM Transactions on Applied Mathematics},
year = {2021},
volume = {2},
number = {4},
pages = {724--747},
abstract = {<p style="text-align: justify;">In this paper, we propose and develop a novel saturation component based
fuzzy Mumford-Shah model for color image segmentation. The main feature of this
model is that we determine different segments by using the saturation component in
hue, saturation, and value (HSV) color space instead of the original red, green and blue
(RGB) color space. The proposed model is formulated for multiphase segmentation of
color images with the assumption that a piecewise smooth function is approximated
by the product of a piecewise constant function and a smooth function. The piecewise
constant function and the smooth function are used to represent different segments
and to estimate the bias field respectively in the color image. The approximation is
calculated based on the saturation component which is particularly useful to distinguish edges and capture the inherent correlation among red, green and blue channels
in color images. Experimental results are presented to demonstrate that the segmentation performance of the proposed model is much better than existing color image
segmentation methods.</p>},
issn = {2708-0579},
doi = {https://doi.org/10.4208/csiam-am.SO-2021-0010},
url = {https://global-sci.com/article/82362/a-saturation-component-based-fuzzy-mumford-shah-model-for-color-image-segmentation}
}