一種融合曲線演化與模糊C均值聚類算法的快速圖像分割模型

馬英然; 彭延軍

doi:10.11999/JEIT160786

一種融合曲線演化與模糊C均值聚類算法的快速圖像分割模型

doi: 10.11999/JEIT160786

馬英然¹,
彭延軍¹

基金項(xiàng)目:

國家自然科學(xué)基金(61502279)，國家重點(diǎn)研發(fā)計(jì)劃課題(2016YFC0801406)，山東省自然科學(xué)基金(ZR2015FM013)，山東省重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2016GSF120012)，泰山學(xué)者工程項(xiàng)目

計(jì)量
- 文章訪問數(shù): 1499
- HTML全文瀏覽量: 198
- PDF下載量: 468
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-07-22
- 修回日期: 2017-02-09
- 刊出日期: 2017-06-19

Fast Image Segmentation Model Combined with Fuzzy C-means Method and Curve Evolution

MA Yingran¹,
PENG Yanjun¹

Funds:

The National Natural Science Foundation of China (61502279), The National Key Research and Development Projects (2016YFC0801406), Shandong Provincial Natural Science Foundation (ZR2015FM013), Shandong Province Key Research and Development Projects (2016GSF120012), Taishan Scholar Project

摘要

摘要: 針對模糊C均值聚類算法(FCM)分割圖像時(shí)對噪聲敏感和分割邊界不封閉問題，該文基于FCM的隸屬度矩陣定義偽水平集及演化曲線，提出一個(gè)融合曲線演化和FCM的快速圖像分割模型。在偽水平集上，通過采用高斯濾波近似曲線演化過程中的弧長正則項(xiàng)，得到封閉光滑的分割邊界；通過設(shè)計(jì)新的邊緣停止函數(shù)，依據(jù)灰度值與隸屬度映射關(guān)系對噪聲點(diǎn)灰度值進(jìn)行修正，降低了濾波對聚類的影響。聚類和曲線平滑交替進(jìn)行，提高了模型對圖像噪聲的魯棒性。實(shí)驗(yàn)結(jié)果表明該模型能夠較好地克服圖像噪聲對分割的影響，得到較為理想的分割結(jié)果。
- 圖像分割 /
- 模糊聚類 /
- 活動(dòng)輪廓 /
- 曲線演化
Abstract: To solve the problems about noise sensitivity and unclosed segmentation object boundaries in Fuzzy C-means Method (FCM), this paper proposes a fast image segmentation model combined with FCM and curve evolution, based on the pseudo level set formulations and object boundary curves, which are defined on the membership matrixes of FCM. To get the smooth and closed segmentation object boundaries, the Gaussian filter is performed on the pseudo level sets to approximate the function of the curve length regularization term. To eliminate the influence of Gaussian filter on the results of FCM, the gray values of the noisy points?are corrected, according to a new introduced edge-stop function and the mapping relationship between the gray value and membership degree. The FCM and the smoothing object boundary stage are performed alternately, which improves the robustness of this model. The experimental results show that the proposed model can overcome the influence of noise and get better segmentation results.
- Image segmentation /
- Fuzzy C-Means (FCM) /
- Active contour /
- Curve evolution

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參考文獻(xiàn)(22)

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