基于交叉视觉皮质模型的骨架伪分支剔除方法  

Algorithm of biased skeleton trim based on intersecting cortical model

作　　者：周理[1] 何林远[1] 孙毅 毕笃彦[1] 高山[1] 

机构地区：[1]空军工程大学工程学院,西安710038 [2]上海胶带股份有限公司军事代表室,上海200235

出　　处：《计算机应用》2012年第9期2553-2555,2627,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61175029);国防科技重点实验室基金资助项目(9140C610301080C6106);航空科学基金资助项目(20101996009)

摘　　要：为解决骨架伪分支剔除过程中目标几何尺寸失真和处理效率低下的问题,提出一种基于交叉视觉皮质模型的图像骨架伪分支剔除算法。首先,依据骨架伪分支的固有特征,引入并修正了骨架分支端点和连接点的定义,以准确获取骨架分支与伪分支的位置信息;然后,利用这些点的位置信息和交叉视觉皮质模型循环点火次数,构建出交叉视觉皮质神经元传播的熄火条件;最后,在熄火条件的指引下,借助点火神经元动态发放的脉冲具有并行传播的生物性能,从而快速判断并准确剔除伪分支。与传统数学形态学方法的比较实验结果表明,该算法不仅计算速度快,抗噪能力强,而且能够保持骨架结构的完整性。In order to solve the problem of geometric distortion and low efficiency in the process of biased skeleton trim, a new algorithm of biased skeleton trim based on intersecting cortical model was proposed. At first, according to inherent features of skeleton biased branch, definitions of endpoint and junction point were introduced and revised in the algorithm to accurately locate skeleton branch and biased branch. Then, with that information and the iteration number of intersecting cortical model, flameout condition of neurons spreading was set up. Finally, guided by that condition, the biased skeleton branch can be judged fast and trimmed accurately, with the aid of impulse dynamically generated by ignition neurons, which has biological nature of parallel transmission. Compared with conventional methods based on mathematical morphology, the experimental results show that the proposed algorithm has good performance in structural integrity of skeleton, as well as computation speed and anti-noise ability.

关 键 词：交叉视觉皮质模型 点火神经元 骨架 伪分支 熄火条件 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]