摘要
张量投票算法是感知聚类方法中一种比较常用的计算方法,可以应用到图像处理等各个方面,具有较强的鲁棒性,非迭代等特性。张量投票算法中尺度参数的自适应选取对于投票域的建立起着至关重要的作用。通过分形维数来选取尺度参数,建立了尺度参数与分形维数的关系,提出了基于分形维数的自适应张量投票算法,并将该方法应用于图像的线特征提取和边缘修复。与传统的张量投票算法进行比较,该方法在图像线特征提取和边缘修复方面获得了较好的实验结果。
Tensor voting algorithm with strong robustness and non-iteration is a common computing method in perceptual group- ing and it is wildly used in image processing. Selecting the scale parameter adaptively in tensor voting algorithm plays an important role in creating the voting fields. In this paper, the scale parameter is selected by fractal dimension. The relationship between scale parameter and fractal dimension is established and then, the adapted tensor voting algorithm based on fractal dimension is proposed. The proposed method is applied in line feature extraction and edge inpainting. The better experimental results are obtained in line feature extraction image and edge repair by the comparison of the proposed method and conventional tensor voting algorithm.
出处
《计算机工程与应用》
CSCD
2013年第12期168-171,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.60971127)
西安理工大学高层次人员启动基金(No.108-210905)
陕西省教育厅科研计划项目(No.09JK617)
关键词
感知聚类
张量投票算法
分形维数
尺度参数
perceptual grouping
tensor voting algorithm
fractal dimension
scale parameter
