Gradient Estimation Using Wide Support Operators
Gradient Estimation Using Wide Support Operators
复制标题
DOI：
10.1109/tip.2008.2011758
复制
发表时间：
2009-04
影响因子：
10.6
通讯作者：
H. Senel
中科院分区：
文献类型：
--
作者：
H. Senel
定位图像边缘的最快方法之一是基于小梯度核，如Sobel，Prewitt和Roberts。虽然小梯度核提供了一种快速计算梯度的方法，但它们对噪声、边缘位置和边缘方向几乎没有控制。已知它们仅对阶跃边缘敏感，而不能检测平滑边界。另一方面，大的核提供上级噪声抑制特性，但是它们遭受边缘周围的宽响应区域。由于它们的宽支撑，它们会导致相邻对象的边合并。与大梯度内核相关的问题阻碍了它们的广泛使用。本文提出了一种基于模糊拓扑的方法，以方便使用较大的梯度核。新方法有效地限制了边缘周围的响应区域，并防止相邻对象相互影响。合成图像用于显示上级噪声抑制特性和对阶跃和斜坡边缘的响应特性。自然图像也被用来评估新提出的拓扑梯度估计的性能定性。
One of the fastest methods of localizing edges in images is based on small gradient kernels, such as Sobel, Prewitt, and Roberts. Although small gradient kernels provide a fast way of computing the gradients, they have little control over noise, edge location, and edge orientation. They are known to be only sensitive to step edges and fail to detect smooth boundaries. On the other hand, large kernels provide superior noise suppression characteristics, but they suffer from wide response area around edges. They cause edges of neighboring objects to merge due to their wide support. Problems associated with large gradient kernels prevent their widespread usage. This paper presents a fuzzy topology-based method to facilitate the use of larger gradient kernels. The new method effectively limits the response area around the edge and prevents neighboring objects to affect each other. Synthetic images are used to show the superior noise suppression properties and response characteristics to both step and ramp edges. Natural images are also used to assess the performance of the newly proposed topological gradient estimation qualitatively.