非参数化区域竞争方法:一种新的图像分割框架

提出了一种新的图像分割框架--非参数化区域竞争算法.这种算法克服了基于尺度 空间滤波的特征空间聚类法的缺陷,提高了原区域竞争算法的性能,并且采取了一种自动选取 种子位置及大小的形式化策略.非参数化区域竞争算法可以把图像分割成统计意义上并不具有 一致性,但在应用中更有意义的区域,称这样的分割为语义一致(或均匀)的分割.非参数化区域 竞争算法把定量地控制分割结果中的区域个数和语义一致的分割结合起来,从而净化了分割结 果,并且可以降低后继算法的复杂度.

Non-Parametric Region Competition:A New Scheme for Image Segmentation

This paper presents a non parametric region competition scheme which combines scale space clustering and region competition to segment the image. It also proposes a formal and general procedure to automatically find the initial regions. Our algorithm can segment an image into regions which are not homogeneous in the sense of statistics, but homogeneous in the sense of semantics with respect to the segmentation context. We call it semantically homogeneous segmentation of the image. Using both semantic homogeneity and quantitative control of the number of the resultant homogeneous regions, our algorithm may produce a 'clean' resultant image, thus simplifying the following procedures.