计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
10期
208-211
,共4页
王海军%张圣燕%柳明%马文来
王海軍%張聖燕%柳明%馬文來
왕해군%장골연%류명%마문래
图像分割%主动轮廓%局部二值拟合(LBF)模型%局部图像拟合(LIF)模型%局部高斯分布拟合(LGDF)模型
圖像分割%主動輪廓%跼部二值擬閤(LBF)模型%跼部圖像擬閤(LIF)模型%跼部高斯分佈擬閤(LGDF)模型
도상분할%주동륜곽%국부이치의합(LBF)모형%국부도상의합(LIF)모형%국부고사분포의합(LGDF)모형
image segmentation%active contour%Local Binary Fitting(LBF)model%Local Image Fitting(LIF)model%Local Gaussian Distribution Fitting(LGDF)model
在现有的活动轮廓中,LBF模型、LIF模型和LGDF模型是著名的基于区域的模型。虽然能分割灰度不均匀的图像,但对活动轮廓的初始化和噪声较为敏感。针对该问题,提出一种融合全高斯和局部高斯概率信息的活动轮廓模型。首先由全局高斯模型的全局灰度拟合力和局部高斯模型的局部灰度拟合力的一个线性组合来构造水平集演化力,然后引入这两个拟合力的动态权重以达到该模型的灵活性,实验结果表明,该模型能分割灰度不均的图像,且允许灵活的轮廓初始化,抗噪声性强。
在現有的活動輪廓中,LBF模型、LIF模型和LGDF模型是著名的基于區域的模型。雖然能分割灰度不均勻的圖像,但對活動輪廓的初始化和譟聲較為敏感。針對該問題,提齣一種融閤全高斯和跼部高斯概率信息的活動輪廓模型。首先由全跼高斯模型的全跼灰度擬閤力和跼部高斯模型的跼部灰度擬閤力的一箇線性組閤來構造水平集縯化力,然後引入這兩箇擬閤力的動態權重以達到該模型的靈活性,實驗結果錶明,該模型能分割灰度不均的圖像,且允許靈活的輪廓初始化,抗譟聲性彊。
재현유적활동륜곽중,LBF모형、LIF모형화LGDF모형시저명적기우구역적모형。수연능분할회도불균균적도상,단대활동륜곽적초시화화조성교위민감。침대해문제,제출일충융합전고사화국부고사개솔신식적활동륜곽모형。수선유전국고사모형적전국회도의합력화국부고사모형적국부회도의합력적일개선성조합래구조수평집연화력,연후인입저량개의합력적동태권중이체도해모형적령활성,실험결과표명,해모형능분할회도불균적도상,차윤허령활적륜곽초시화,항조성성강。
In the existing active contour models, Local Binary Fitting(LBF)model、Local Image Fitting(LIF)model and Local Gaussian Distribution Fitting(LGDF)model are popular region-based models. The three models are able to deal with intensity inhomogeneity, however, they are sensitive to initialization and noise. In order to address this problem, an active contour model combining the global and local Gaussian probability information is proposed. First the force of level set evolution is defined as a linear combination of the global and local Gaussian distribution model. Then the dynamic weights of the forces are introduced to allow the flexible active contour model. Experimental results show the the pro-posed model can segment images with intensity inhomogeneity, while it allows flexible initialization and is less sensitive to noise.