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Volume 14, Issue 4
Image Segmentation via Fischer-Burmeister Total Variation and Thresholding

Tingting Wu, Yichen Zhao, Zhihui Mao, Li Shi, Zhi Li & Yonghua Zeng

Adv. Appl. Math. Mech., 14 (2022), pp. 960-988.

Published online: 2022-04

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  • Abstract

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.

  • AMS Subject Headings

65K10, 68U10, 94A08

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COPYRIGHT: © Global Science Press

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@Article{AAMM-14-960, 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 = {

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.

}, issn = {2075-1354}, doi = {https://doi.org/10.4208/aamm.OA-2021-0126}, url = {http://global-sci.org/intro/article_detail/aamm/20442.html} }
TY - JOUR T1 - Image Segmentation via Fischer-Burmeister Total Variation and Thresholding JO - Advances in Applied Mathematics and Mechanics VL - 4 SP - 960 EP - 988 PY - 2022 DA - 2022/04 SN - 14 DO - http://doi.org/10.4208/aamm.OA-2021-0126 UR - https://global-sci.org/intro/article_detail/aamm/20442.html KW - Image segmentation, Fischer-Burmeister total variation, difference of convex algorithm, sPADMM, K-means method. AB -

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.

Tingting Wu, Yichen Zhao, Zhihui Mao, Li Shi, Zhi Li & Yonghua Zeng. (2022). Image Segmentation via Fischer-Burmeister Total Variation and Thresholding. Advances in Applied Mathematics and Mechanics. 14 (4). 960-988. doi:10.4208/aamm.OA-2021-0126
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