Abstract: LBF model is one of the well-known region-based active contour models.In contrast to the Piecewise Constant （PC） models，it introduces a Local Binary Fitting（LBF） energy with a Gaussian kernel function.Because the LBF energy enables the extraction of accurate local image information，LBF model can address the segmentation of images with intensity inhomogeneity，to which PC models are not applicable.This paper proposes an improvement of LBF model，which utilizes a new kernel function instead of Gaussian kernel function.Experimental results show that the new LBF model is about 50% faster than the original LBF model.

摘要： LBF模型是一个著名的基于区域的活动轮廓模型。与PC（Piecewise Constant）模型不同，该模型引入了一个以高斯函数为核函数的局部二值拟合（Local Binary Fitting，LBF）能量。因为LBF能量能够获取图像的局部信息，所以LBF模型解决了PC模型不能处理灰度不均一图像的分割问题。提出了一个改进的LBF模型，它使用一个新的核函数代替高斯核函数。实验表明：与LBF模型比较，新模型减少分割时间约50%。