@Article{AAMM-14-4,
author = {},
title = {Image Segmentation via Fischer-Burmeister Total Variation and Thresholding},
journal = {Advances in Applied Mathematics and Mechanics},
year = {2022},
volume = {14},
number = {4},
pages = {960--988},
abstract = {<p style="text-align: justify;">Image segmentation is a significant problem in image processing. In this
paper, we propose a new two-stage scheme for segmentation based on the Fischer-Burmeister total variation (FBTV). The first stage of our method is to calculate a smooth
solution from the FBTV Mumford-Shah model. Furthermore, we design a new difference of convex algorithm (DCA) with the semi-proximal alternating direction method
of multipliers (sPADMM) iteration. In the second stage, we make use of the smooth solution and the K-means method to obtain the segmentation result. To simulate images
more accurately, a useful operator is introduced, which enables the proposed model
to segment not only the noisy or blurry images but the images with missing pixels
well. Experiments demonstrate the proposed method produces more preferable results comparing with some state-of-the-art methods, especially on the images with
missing pixels.</p>},
issn = {2075-1354},
doi = {https://doi.org/10.4208/aamm.OA-2021-0126},
url = {https://global-sci.com/article/72927/image-segmentation-via-fischer-burmeister-total-variation-and-thresholding}
}