改进Split-Merge分割用于蝗虫图像  

Improved Split-Merge segmentation used for locust image

作　　者：张煜东[1] 吴乐南[1] 韦耿[1] 吴含前[2] 

机构地区：[1]东南大学信息科学与工程学院,南京210096 [2]东南大学软件学院,南京210096

出　　处：《计算机工程与应用》2009年第14期34-38,共5页Computer Engineering and Applications

基　　金：国家高技术研究发展计划(863)No.2008AA01Z227;国家自然科学基金No.60872075;江苏省自然科学基金No.BK2007103;高等学校科技创新工程重大项目培育资金项目(No.706028)~~

摘　　要：为了更好地分割蝗虫图像,对传统Split-Merge算法做出改进:用PCNN来进行分裂,采用一种简化的Mumford-Shah模型进行合并。该方法的优点是:分裂阶段不仅无效分割数目减少,而且无方块效应,对边缘定位准确;合并阶段能够理想地将分裂后的区域合并为感兴趣的前景与不感兴趣的背景,误合并与欠合并大幅减小。实验对成虫与幼虫图像均进行分割。综合比较,该算法性能较优。In order to segment locust images more effectively,an improved Split-Merge algorithm is proposed.It gives split on images via PCNN,and meantime merges areas via a simplified Mumford-Shah model.Its advantages are as follows：At split stage,it can reduce the number of invalid split,be immune of block-effect,and locate edges more accurately;at merge stage,it can merge areas into interesting foregrounds and uninteresting backgrounds perfectly,and reduce the chance of incorrect and insufficient merge.Experiments with imago and larva locust images is segmented.It can conclude that this proposed method performs best after comprehensive comparison.

关 键 词：图像分割 分裂合并法 脉冲耦合神经网络 

分 类 号：TN911.73[电子电信—通信与信息系统]