基于Sobel算子的图像快速二维最大熵阈值分割算法

计算机科学 ›› 2015, Vol. 42 ›› Issue (Z6): 209-210.

基于Sobel算子的图像快速二维最大熵阈值分割算法

李锋,阚建霞

江苏科技大学电子信息学院镇江212000,江苏科技大学电子信息学院镇江212000

出版日期:2018-11-14 发布日期:2018-11-14

Fast Two-dimensional Maximum Entropy Threshold Segmentation Method Based on Sobel Operator

LI Feng and KAN Jian-xia

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 经典的二维最大熵阈值分割算法计算时间长,贮存信息需要的空间大。针对这些问题,在标准二维最大熵阈值分割算法的基础上,提出了一种基于二维最大熵阈值递推的快速算法,同时还将采用Sobel算子边缘检测得到的阈值应用到快速二维最大熵阈值分割算法中,以此来解决图像中出现的细节丢失等问题。最后,实验证明这种改进的算法通过运用递推公式将处理时间从原来的O(L⁴)减少到O(L²),不仅降低了计算的复杂性,提高了效率,同时也保护了细节信息。

Abstract: The classic two-dimensional maximum entropy threshold segmentation algorithm takes a long time to compute and consumes large space to store information.To solve these problems,in this paper a fast threshold recursion method was proposed based on the standard two-dimensional maximum entropy threshold segmentation algorithm,at the same time,the threshold obtained by Sobel operator edge diction was applied to the fast threshold segmentation algorithm in order to solve the problem of the loss of details.Finally,the experiments show that this improved algorithm makes processing time reduce from O(L⁴) to O(L²) through the recursive formula.It not only reduces the complexity of the calculation,but also protects the details.

Key words: Two-dimensional maximum entropy algorithm,Fast recursive,Edge of stack,Sobel algorithm,Image segmentation

李锋,阚建霞. 基于Sobel算子的图像快速二维最大熵阈值分割算法[J]. 计算机科学, 2015, 42(Z6): 209-210. https://doi.org/

LI Feng and KAN Jian-xia. Fast Two-dimensional Maximum Entropy Threshold Segmentation Method Based on Sobel Operator[J]. Computer Science, 2015, 42(Z6): 209-210. https://doi.org/

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