不用高斯平滑的边缘活动轮廓模型

在基于边缘的活动轮廓模型中，边缘停止函数的选择是十分重要的。边缘停止函数是一个单调递减的正函数和高斯平滑后的图像梯度模的复合函数。基于这种边缘停止函数的活动轮廓模型存在两个缺点：一是在同质区域演化速度慢；二是图像需要预先进行高斯平滑（滤波），但平滑噪声的同时，也平滑了目标边缘，可能使分割不够准确。提出一个新的不用高斯平滑的边缘停止函数。实验表明，基于这种边缘停止函数的活动轮廓模型能够减少迭代次数与分割时间约50%。

Edge-based active contour model without Gaussian smoothing

The edge stopping function is very important in edge-based active contour models.It is typically the composition of a strictly monotonically decreasing positive function and the gradient magnitude of Gaussian smoothed image.The active contour models based on this type of edge stopping function have the drawbacks of long evolving time and need for Gauss-ian filter.Although Gaussian filter smoothes noisei,t may also smooth edgesi;t is possible for the active contour models to inaccurately locate the edges.A new edge stopping function without Gaussian smoothing is proposed to overcome the two drawbacks above.Experimental results show that an active contour model based on the new edge stopping function is about 50% faster than the original model.