摘要
为解决内容驱动的彩色图像分割问题,结合形态学跳连通算子和谱图聚类技术建立基于粒的分割方法.在粒计算的框架下,采用彩色图像的连通性算子构造图像分割问题的基本粒.在估计基本粒尺度的基础上,利用曲面正交多项式拟合技术进行基本粒规则化.通过谱图聚类算法融合各个基本粒,实现粒视角下彩色图像的分割.模拟实验验证了本文所提方法的有效性,面向粒的分割技术在消除边界块效应和降低计算复杂度方面优于基于规则分块和面向像素的分割方法.
A granule-viewed segmentation method is established based on morphological jump connected operator and spectral graph clustering to solve the problem of content-driven color image segmentation.In the framework of granular computing,basic granules in image segmentation are constructed by color jump connected operator.Based on evaluation of the size of basic granules,regularization of basic granules is conducted by orthogonal polynomial surface fitting.Then spectral graph clustering is used to fuse basic granules and segmentation of a color image is achieved.The validity of the proposed method is demonstrated by experiments.The granule-viewed technique exceeds to regular-blocking-based and pixel-based methods in both elimination of edge-blocking effect and reduction of computational complexity.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2013年第3期112-116,共5页
Journal of Harbin Institute of Technology
基金
国家自然科学基金资助项目(10771043)
关键词
彩色图像分割
形态学连通
正交多项式曲面拟合
谱图聚类
color image segmentation
morphological connectivity
orthogonal polynomial surface fitting
spectral graph clustering
